From 29238fa467a4d5470b6f1dd2cd452ee00f3f1609 Mon Sep 17 00:00:00 2001 From: Jan Janssen Date: Thu, 18 Apr 2024 10:11:21 -0500 Subject: [PATCH 1/5] Add jupyter notebooks with mybinder --- README.md | 16 +- .../aiida_qe_basic => adis_tools}/__init__.py | 0 .../adis_tools => adis_tools}/parsers.py | 0 .../schemas}/__init__.py | 0 .../schemas/qes_230310.xsd | 0 aiida.ipynb | 1 + .../aiida_qe_basic}/__init__.py | 0 .../aiida_qe_basic/pw.py | 0 .../pyproject.toml | 0 environment.yml | 14 + eos/README.md | 12 - eos/Si.cif | 28 - eos/aiida/.gitignore | 1 - eos/aiida/README.md | 43 - eos/aiida/environment.yml | 10 - eos/aiida/eos_wc.py | 74 - eos/aiida/ev_curve.ipynb | 795 - eos/aiida/ev_curve_basic.ipynb | 751 - eos/aiida/files/Al.cif | 33 - eos/aiida/qe-plugin-inputs/eos-scf/aiida.in | 39 - .../pseudo/Si.pbesol-n-rrkjus_psl.1.0.0.UPF | 16563 -------------- eos/aiida/qe-plugin-inputs/relax/aiida.in | 41 - .../pseudo/Si.pbesol-n-rrkjus_psl.1.0.0.UPF | 16563 -------------- eos/aiida/qe-setup.ipynb | 188 - eos/atomate2/eos.png | Bin 30680 -> 0 bytes eos/atomate2/ev_curve_qe.ipynb | 392 - .../ev_curve_universal_forcefields.ipynb | 369 - .../pseudo/Al.pbe-n-kjpaw_psl.1.0.0.UPF | 18497 ---------------- eos/atomate2/requirements.txt | 2 - eos/basic/.gitignore | 3 - eos/basic/pseudo/Al.pbe-n-kjpaw_psl.1.0.0.UPF | 18497 ---------------- eos/basic/quantumespresso-ev.ipynb | 228 - eos/pyiron/lammps.ipynb | 1438 -- eos/pyiron/pyiron_like_workflows.ipynb | 1602 -- eos/pyiron/quantumespresso.ipynb | 1 - .../pseudo}/Al.pbe-n-kjpaw_psl.1.0.0.UPF | 0 jobflow.ipynb | 1 + parsers/README.md | 22 - parsers/adis-tools/pyproject.toml | 17 - parsers/relax.xml | 12492 ----------- parsers/sandbox.ipynb | 101 - postBuild | 1 + pyiron_base.ipynb | 1 + semantics.md | 166 - 44 files changed, 32 insertions(+), 88970 deletions(-) rename {eos/aiida/aiida_qe_basic/aiida_qe_basic => adis_tools}/__init__.py (100%) rename {parsers/adis-tools/adis_tools => adis_tools}/parsers.py (100%) rename {parsers/adis-tools/adis_tools => adis_tools/schemas}/__init__.py (100%) rename {parsers/adis-tools/adis_tools => adis_tools}/schemas/qes_230310.xsd (100%) create mode 100644 aiida.ipynb rename {parsers/adis-tools/adis_tools/schemas => aiida_qe_basic/aiida_qe_basic}/__init__.py (100%) rename {eos/aiida/aiida_qe_basic => aiida_qe_basic}/aiida_qe_basic/pw.py (100%) rename {eos/aiida/aiida_qe_basic => aiida_qe_basic}/pyproject.toml (100%) create mode 100644 environment.yml delete mode 100644 eos/README.md delete mode 100644 eos/Si.cif delete mode 100644 eos/aiida/.gitignore delete mode 100644 eos/aiida/README.md delete mode 100644 eos/aiida/environment.yml delete mode 100644 eos/aiida/eos_wc.py delete mode 100644 eos/aiida/ev_curve.ipynb delete mode 100644 eos/aiida/ev_curve_basic.ipynb delete mode 100644 eos/aiida/files/Al.cif delete mode 100644 eos/aiida/qe-plugin-inputs/eos-scf/aiida.in delete mode 100644 eos/aiida/qe-plugin-inputs/eos-scf/pseudo/Si.pbesol-n-rrkjus_psl.1.0.0.UPF delete mode 100644 eos/aiida/qe-plugin-inputs/relax/aiida.in delete mode 100644 eos/aiida/qe-plugin-inputs/relax/pseudo/Si.pbesol-n-rrkjus_psl.1.0.0.UPF delete mode 100644 eos/aiida/qe-setup.ipynb delete mode 100644 eos/atomate2/eos.png delete mode 100644 eos/atomate2/ev_curve_qe.ipynb delete mode 100644 eos/atomate2/ev_curve_universal_forcefields.ipynb delete mode 100644 eos/atomate2/pseudo/Al.pbe-n-kjpaw_psl.1.0.0.UPF delete mode 100644 eos/atomate2/requirements.txt delete mode 100644 eos/basic/.gitignore delete mode 100644 eos/basic/pseudo/Al.pbe-n-kjpaw_psl.1.0.0.UPF delete mode 100644 eos/basic/quantumespresso-ev.ipynb delete mode 100644 eos/pyiron/lammps.ipynb delete mode 100644 eos/pyiron/pyiron_like_workflows.ipynb delete mode 100644 eos/pyiron/quantumespresso.ipynb rename {eos/aiida/files => espresso/pseudo}/Al.pbe-n-kjpaw_psl.1.0.0.UPF (100%) create mode 100644 jobflow.ipynb delete mode 100644 parsers/README.md delete mode 100644 parsers/adis-tools/pyproject.toml delete mode 100644 parsers/relax.xml delete mode 100644 parsers/sandbox.ipynb create mode 100644 postBuild create mode 100644 pyiron_base.ipynb delete mode 100644 semantics.md diff --git a/README.md b/README.md index 8534024..24435fd 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,14 @@ -# ADIS2023 -ADIS 2023 +# Quantum Espresso Workflow +The structure optimization of an Aluminium bulk structure followed by the calculation of the bulk modulus by computing the energy for different volumes is implemented in three different workflow frameworks, [Aiida](https://aiida.net), [jobflow](https://materialsproject.github.io/jobflow/) and [pyiron_base](https://pyiron.org). + +Test it on: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/materialdigital/ADIS2023/HEAD) + +## Explanation +* `adis_tools` - quantum espresso parser independent of the workflow frameworks. +* `aiida_qe_basic` - simple Aiida interface. +* `espresso/pseudo/Al.pbe-n-kjpaw_psl.1.0.0.UPF` - This is the pseudo potential for qunatum espresso. By placing it in `~/espresso/pseudo`, it is automatically detected by quantum espresso. +* `aiida.ipynb` - workflow implemented in [Aiida](https://aiida.net) +* `environment.yml` - Conda environment to define the dependencies. +* `jobflow.ipynb` - workflow implemented in [jobflow](https://materialsproject.github.io/jobflow/) +* `postBuild` - script to install `aiida_qe_basic` in the mybinder environment. +* `pyiron_base.ipynb` - workflow implemented in [pyiron_base](https://pyiron.org) diff --git a/eos/aiida/aiida_qe_basic/aiida_qe_basic/__init__.py b/adis_tools/__init__.py similarity index 100% rename from eos/aiida/aiida_qe_basic/aiida_qe_basic/__init__.py rename to adis_tools/__init__.py diff --git a/parsers/adis-tools/adis_tools/parsers.py b/adis_tools/parsers.py similarity index 100% rename from parsers/adis-tools/adis_tools/parsers.py rename to adis_tools/parsers.py diff --git a/parsers/adis-tools/adis_tools/__init__.py b/adis_tools/schemas/__init__.py similarity index 100% rename from parsers/adis-tools/adis_tools/__init__.py rename to adis_tools/schemas/__init__.py diff --git a/parsers/adis-tools/adis_tools/schemas/qes_230310.xsd b/adis_tools/schemas/qes_230310.xsd similarity index 100% rename from parsers/adis-tools/adis_tools/schemas/qes_230310.xsd rename to adis_tools/schemas/qes_230310.xsd diff --git a/aiida.ipynb b/aiida.ipynb new file mode 100644 index 0000000..c7641b7 --- /dev/null +++ b/aiida.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3 (ipykernel)","language":"python"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.0"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Setup for running Quantum ESPRESSO","metadata":{}},{"cell_type":"code","source":"import subprocess","metadata":{"tags":[],"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"try: \n subprocess.check_output([\"verdi\", \"profile\", \"setup\", \"core.sqlite_dos\", \"-n\", \"--profile\", \"test\", \"--email\", \"no@email.com\"])\nexcept: \n pass","metadata":{"tags":[],"trusted":true},"execution_count":2,"outputs":[{"name":"stderr","text":"/srv/conda/envs/notebook/lib/python3.11/site-packages/aiida/manage/configuration/settings.py:59: UserWarning: Creating AiiDA configuration folder `/home/jovyan/.aiida`.\n warnings.warn(f'Creating AiiDA configuration folder `{path}`.')\n","output_type":"stream"}]},{"cell_type":"code","source":"from pathlib import Path\nfrom ase.build import bulk\n\nfrom aiida import orm, engine, load_profile\nfrom aiida.common.exceptions import NotExistent\n\nload_profile()","metadata":{"tags":[],"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"Profile"},"metadata":{}}]},{"cell_type":"code","source":"try:\n localhost = orm.load_computer('localhost')\nexcept NotExistent:\n localhost = orm.Computer(\n label='localhost',\n hostname='localhost',\n transport_type='core.local',\n scheduler_type='core.direct',\n workdir=Path('workdir').absolute().as_posix()\n ).store()\n localhost.configure()\n\ntry:\n pw_code = orm.load_code('pw@localhost')\nexcept NotExistent:\n pw_code = orm.InstalledCode(\n label='pw',\n computer=localhost,\n filepath_executable='pw.x',\n default_calc_job_plugin='aiida_qe_basic.pw',\n prepend_text='export OMP_NUM_THREADS=1'\n ).store()","metadata":{"tags":[],"trusted":true},"execution_count":4,"outputs":[{"name":"stderr","text":"/srv/conda/envs/notebook/lib/python3.11/site-packages/aiida/orm/nodes/data/code/legacy.py:42: AiidaDeprecationWarning: The `Code` class is deprecated. To create an instance, use the `aiida.orm.nodes.data.code.installed.InstalledCode` or `aiida.orm.nodes.data.code.portable.PortableCode` for a \"remote\" or \"local\" code, respectively. If you are using this class to compare type, e.g. in `isinstance`, use `aiida.orm.nodes.data.code.abstract.AbstractCode`. (this will be removed in v3)\n warn_deprecation(\n","output_type":"stream"}]},{"cell_type":"code","source":"from aiida_qe_basic.pw import PwCalculation\n\nbuilder = PwCalculation.get_builder()\n\nbuilder.code = pw_code\nbuilder.structure = orm.StructureData(ase=bulk('Al', a=4.05, cubic=True))\nbuilder.pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\nbuilder.parameters = orm.Dict(\n {\n 'CONTROL': {\n 'calculation': 'scf',\n # 'pseudo_dir': Path('files').absolute().as_posix(),\n },\n 'SYSTEM': {\n 'occupations': 'smearing',\n 'smearing': 'cold',\n 'degauss': 0.02\n }\n }\n)\nbuilder.metadata.options.resources = {\n 'num_machines': 1,\n 'num_mpiprocs_per_machine': 1\n}","metadata":{"tags":[],"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"! rabbitmq-server -detached","metadata":{"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"! sleep 5","metadata":{"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"results = engine.run(builder)","metadata":{"tags":[],"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"results","metadata":{"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"{'structure': ,\n 'properties': ,\n 'remote_folder': ,\n 'retrieved': }"},"metadata":{}}]},{"cell_type":"code","source":"results['properties'].get_dict()","metadata":{"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"{'energy': -1074.9272223013, 'volume': 66.430124128914}"},"metadata":{}}]},{"cell_type":"markdown","source":"# Equation of State curve - basic QE\n\nRunning an EOS without all the fancy features in the `aiida-quantumespresso` plugin.","metadata":{}},{"cell_type":"code","source":"from pathlib import Path\n\nfrom aiida import orm, engine, load_profile\n\nload_profile()","metadata":{"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"Profile"},"metadata":{}}]},{"cell_type":"markdown","source":"## Importing a structure","metadata":{}},{"cell_type":"code","source":"from ase.build import bulk\n\nstructure = orm.StructureData(ase=bulk('Al', a=4.05, cubic=True))","metadata":{"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"markdown","source":"## Relaxing the geometry","metadata":{}},{"cell_type":"code","source":"resources = {\n 'num_machines': 1,\n 'num_mpiprocs_per_machine': 1\n}\n\nrelax_params = {\n 'CONTROL': {\n 'calculation': 'vc-relax',\n # 'pseudo_dir': Path('files').absolute().as_posix(),\n },\n 'SYSTEM': {\n 'occupations': 'smearing',\n 'smearing': 'cold',\n 'degauss': 0.02\n }\n}","metadata":{"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"from aiida_qe_basic.pw import PwCalculation\n\nbuilder = PwCalculation.get_builder()\n\nbuilder.code = orm.load_code('pw@localhost')\nbuilder.structure = orm.StructureData(ase=bulk('Al', a=4.05, cubic=True))\nbuilder.pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\nbuilder.parameters = orm.Dict(relax_params)\nbuilder.metadata.options.resources = resources","metadata":{"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"results = engine.run(builder)\nrelaxed_structure = results['structure']\nrelaxed_structure","metadata":{"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}}]},{"cell_type":"markdown","source":"## Calc function to rescale structures\n\nThe `calcfunction` below takes an input structure and rescales it to different volumes.","metadata":{}},{"cell_type":"code","source":"from aiida_qe_basic.pw import PwCalculation\n\n@engine.calcfunction\ndef rescale_list(structure: orm.StructureData, factor_list: orm.List):\n\n scaled_structure_dict = {}\n\n for index, scaling_factor in enumerate(factor_list.get_list()):\n\n ase_structure = structure.get_ase()\n\n new_cell = ase_structure.get_cell() * scaling_factor\n ase_structure.set_cell(new_cell, scale_atoms=True)\n\n scaled_structure_dict[f'structure_{index}'] = orm.StructureData(ase=ase_structure)\n\n return scaled_structure_dict","metadata":{"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":"Typically, you'd just run it by calling the function as you would a regular Python function:","metadata":{}},{"cell_type":"code","source":"rescaled_structures = rescale_list(relaxed_structure, orm.List(list=[0.9, 0.95, 1.0, 1.05, 1.1]))","metadata":{"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"rescaled_structures","metadata":{"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"{'structure_0': ,\n 'structure_1': ,\n 'structure_2': ,\n 'structure_3': ,\n 'structure_4': }"},"metadata":{}}]},{"cell_type":"markdown","source":"## EOS: Work function version","metadata":{}},{"cell_type":"code","source":"scf_inputs = {\n 'CONTROL': {\n 'calculation': 'scf',\n # 'pseudo_dir': Path('files').absolute().as_posix(),\n },\n 'SYSTEM': {\n 'occupations': 'smearing',\n 'smearing': 'cold',\n 'degauss': 0.02\n }\n}","metadata":{"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"@engine.workfunction\ndef run_eos_wf(code: orm.Code, structure: orm.StructureData, scale_factors: orm.List):\n \"\"\"Run an equation of state of a bulk crystal structure for the given element.\"\"\"\n\n properties = {}\n\n for label, rescaled_structure in rescale_list(structure, scale_factors).items():\n\n builder = PwCalculation.get_builder()\n builder.code = code\n builder.structure = rescaled_structure\n builder.parameters = orm.Dict(scf_inputs)\n builder.pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\n builder.metadata.options.resources = resources\n\n results = engine.run(builder)\n properties[label] = results['properties']\n\n return properties","metadata":{"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"results = run_eos_wf(\n code=orm.load_code('pw@localhost'),\n structure=relaxed_structure,\n scale_factors=[0.9, 0.95, 1.0, 1.05, 1.1]\n)","metadata":{"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"results","metadata":{"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"{'structure_0': ,\n 'structure_1': ,\n 'structure_2': ,\n 'structure_3': ,\n 'structure_4': }"},"metadata":{}}]},{"cell_type":"code","source":"volumes = []\nenergies = []\n\nfor result in results.values():\n volumes.append(result['volume'])\n energies.append(result['energy'])","metadata":{"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nplt.plot(volumes, energies)","metadata":{"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"[]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"markdown","source":"## Work chain version","metadata":{}},{"cell_type":"code","source":"@engine.calcfunction\ndef create_eos_dictionary(**kwargs) -> orm.Dict:\n eos = {\n label: (result['volume'], result['energy'])\n for label, result in kwargs.items()\n }\n return orm.Dict(eos)","metadata":{"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"create_eos_dictionary(**results).get_dict()","metadata":{"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"{'structure_0': [48.283007573324, -1073.9421694118],\n 'structure_1': [56.785519366503, -1074.7251942208],\n 'structure_2': [66.231834805659, -1074.9273047095],\n 'structure_3': [76.671627766898, -1074.7863009779],\n 'structure_4': [88.154572126333, -1074.451028786]}"},"metadata":{}}]},{"cell_type":"code","source":"class EquationOfState(engine.WorkChain):\n \"\"\"WorkChain to compute Equation of State using Quantum ESPRESSO.\"\"\"\n\n @classmethod\n def define(cls, spec):\n \"\"\"Specify inputs and outputs.\"\"\"\n super().define(spec)\n spec.input(\"code\", valid_type=orm.Code)\n spec.input(\"structure\", valid_type=orm.StructureData)\n spec.input(\"scale_factors\", valid_type=orm.List)\n\n spec.outline(\n cls.run_eos,\n cls.results,\n )\n spec.output(\"eos_dict\", valid_type=orm.Dict)\n\n def run_eos(self):\n\n calcjob_dict = {}\n\n for label, rescaled_structure in rescale_list(self.inputs.structure, self.inputs.scale_factors).items():\n\n builder = PwCalculation.get_builder()\n builder.code = self.inputs.code\n builder.structure = rescaled_structure\n builder.parameters = orm.Dict(scf_inputs)\n builder.pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\n builder.metadata.options.resources = resources\n\n calcjob_dict[label] = self.submit(builder)\n\n self.ctx.labels = list(calcjob_dict.keys())\n\n return calcjob_dict\n\n def results(self):\n\n self.report(self.ctx)\n\n eos_results = {\n label: self.ctx[label].outputs['properties'] for label in self.ctx.labels\n }\n eos_dict = create_eos_dictionary(**eos_results)\n self.out('eos_dict', eos_dict)\n","metadata":{"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"engine.run(EquationOfState, code=orm.load_code('pw@localhost'),\n structure=relaxed_structure,\n scale_factors=orm.List([0.9, 0.95, 1.0, 1.05, 1.1]))","metadata":{"trusted":true},"execution_count":28,"outputs":[{"name":"stderr","text":"04/04/2024 05:40:39 PM <83> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [71|EquationOfState|results]: AttributeDict({'labels': ['structure_0', 'structure_1', 'structure_2', 'structure_3', 'structure_4'], 'structure_0': , 'structure_1': , 'structure_2': , 'structure_3': , 'structure_4': })\n","output_type":"stream"},{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"{'eos_dict': }"},"metadata":{}}]},{"cell_type":"markdown","source":"## Using the `builder`","metadata":{}},{"cell_type":"code","source":"builder = EquationOfState.get_builder()","metadata":{"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"builder.structure = relaxed_structure","metadata":{"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"builder","metadata":{"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"Process class: EquationOfState\nInputs:\nmetadata: {}\nstructure: Al\n"},"metadata":{}}]},{"cell_type":"code","source":"builder.scale_factors = orm.List([0.9, 0.95, 1.0, 1.05, 1.1])\nbuilder.code = orm.load_code('pw@localhost')","metadata":{"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"results, node = engine.run_get_node(builder)","metadata":{"trusted":true},"execution_count":33,"outputs":[{"name":"stderr","text":"04/04/2024 05:41:05 PM <83> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [116|EquationOfState|results]: AttributeDict({'labels': ['structure_0', 'structure_1', 'structure_2', 'structure_3', 'structure_4'], 'structure_0': , 'structure_1': , 'structure_2': , 'structure_3': , 'structure_4': })\n","output_type":"stream"}]},{"cell_type":"code","source":"results['eos_dict'].get_dict()","metadata":{"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"{'structure_0': [48.283007573324, -1073.9421694118],\n 'structure_1': [56.785519366503, -1074.7251942208],\n 'structure_2': [66.231834805659, -1074.9273047095],\n 'structure_3': [76.671627766898, -1074.7863009779],\n 'structure_4': [88.154572126333, -1074.451028786]}"},"metadata":{}}]},{"cell_type":"code","source":"eos = node.outputs.eos_dict.get_dict()","metadata":{"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"eos","metadata":{"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"{'structure_0': [48.283007573324, -1073.9421694118],\n 'structure_1': [56.785519366503, -1074.7251942208],\n 'structure_2': [66.231834805659, -1074.9273047095],\n 'structure_3': [76.671627766898, -1074.7863009779],\n 'structure_4': [88.154572126333, -1074.451028786]}"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(\n [v[0] for v in eos.values()],\n [v[1] for v in eos.values()],\n)","metadata":{"tags":[],"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"[]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]}]} \ No newline at end of file diff --git a/parsers/adis-tools/adis_tools/schemas/__init__.py b/aiida_qe_basic/aiida_qe_basic/__init__.py similarity index 100% rename from parsers/adis-tools/adis_tools/schemas/__init__.py rename to aiida_qe_basic/aiida_qe_basic/__init__.py diff --git a/eos/aiida/aiida_qe_basic/aiida_qe_basic/pw.py b/aiida_qe_basic/aiida_qe_basic/pw.py similarity index 100% rename from eos/aiida/aiida_qe_basic/aiida_qe_basic/pw.py rename to aiida_qe_basic/aiida_qe_basic/pw.py diff --git a/eos/aiida/aiida_qe_basic/pyproject.toml b/aiida_qe_basic/pyproject.toml similarity index 100% rename from eos/aiida/aiida_qe_basic/pyproject.toml rename to aiida_qe_basic/pyproject.toml diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..f98fadd --- /dev/null +++ b/environment.yml @@ -0,0 +1,14 @@ +channels: +- conda-forge +dependencies: +- python=3.11 +- pyiron_base=0.8.0 +- qe=7.2 +- qe-tools=2.0.0 +- ase=3.22.1 +- matplotlib=3.8.3 +- xmlschema=3.2.0 +- jobflow=0.1.17 +- pymatgen=2024.3.1 +- aiida-core=2.5.1 +- aiida-core.services diff --git a/eos/README.md b/eos/README.md deleted file mode 100644 index d360b6c..0000000 --- a/eos/README.md +++ /dev/null @@ -1,12 +0,0 @@ -# Equation of state - -This folder hosts the example implementation of the equation of state workflow for the various workflow engines. - -## Logic - -The workflow should take an input structure and implement the following: - -1. Optimize the geometry of the structure (both cell and atomic positions). -2. Starting from the optimized geometry, create a list of new structures with a range of volumes around the equilibrium. -3. Calculate the energy for each volume using an external code (e.g. Quantum ESPRESSO). -4. Combine results and fit with a Birch-Murnaghan. diff --git a/eos/Si.cif b/eos/Si.cif deleted file mode 100644 index deb07da..0000000 --- a/eos/Si.cif +++ /dev/null @@ -1,28 +0,0 @@ -# generated using pymatgen -data_Si -_symmetry_space_group_name_H-M 'P 1' -_cell_length_a 3.86697465 -_cell_length_b 3.86697465 -_cell_length_c 3.86697465 -_cell_angle_alpha 60.00000000 -_cell_angle_beta 60.00000000 -_cell_angle_gamma 60.00000000 -_symmetry_Int_Tables_number 1 -_chemical_formula_structural Si -_chemical_formula_sum Si2 -_cell_volume 40.88829285 -_cell_formula_units_Z 2 -loop_ - _symmetry_equiv_pos_site_id - _symmetry_equiv_pos_as_xyz - 1 'x, y, z' -loop_ - _atom_site_type_symbol - _atom_site_label - _atom_site_symmetry_multiplicity - _atom_site_fract_x - _atom_site_fract_y - _atom_site_fract_z - _atom_site_occupancy - Si Si0 1 0.75000000 0.75000000 0.75000000 1 - Si Si1 1 0.50000000 0.50000000 0.50000000 1 diff --git a/eos/aiida/.gitignore b/eos/aiida/.gitignore deleted file mode 100644 index af51aa4..0000000 --- a/eos/aiida/.gitignore +++ /dev/null @@ -1 +0,0 @@ -workdir \ No newline at end of file diff --git a/eos/aiida/README.md b/eos/aiida/README.md deleted file mode 100644 index bed5043..0000000 --- a/eos/aiida/README.md +++ /dev/null @@ -1,43 +0,0 @@ -# EOS - AiiDA example - -## Setup - -### Environment - -First, create a conda environment from the `environment.yml` file: - -``` -mamba env create -f environment.yml -``` - -This will install `aiida-core`, a binary for Quantum ESPRESSO, and some other tools like `ase` and `jupyterlab` into the `adis-aiida` environment. -Once complete, activate the environment and start the rabbitmq service: - -``` -mamba activate adis-aiida -rabbitmq-server -detached -``` - -I've written a basic parser for QE and added it to the `adis-tools` package: - -``` -pip install -e ../../parsers/adis-tools -``` - -Due to the "plugin nature" of AiiDA, all calculation jobs and parsers must be added to a package and assigned an entry point. -A very basic example for the `pw.x` binary of Quantum ESPRESSO has been added in the `aiida_qe_basic` package: - -``` -pip install -e aiida_qe_basic -``` - -### Profile - -Next, let's set up a testing profile: - -``` -verdi profile setup core.sqlite_dos -n --profile test --email no@email.com -``` - -And run the cells in `qe-setup.ipynb`. -These will set up the `localhost` computer and `pw` code, then do a test run. diff --git a/eos/aiida/environment.yml b/eos/aiida/environment.yml deleted file mode 100644 index ebd5e7e..0000000 --- a/eos/aiida/environment.yml +++ /dev/null @@ -1,10 +0,0 @@ -name: adis-aiida -channels: - - conda-forge -dependencies: - - python=3.11 - - aiida-core~=2.5 - - aiida-core.services - - qe==7.2 - - ase - - jupyterlab diff --git a/eos/aiida/eos_wc.py b/eos/aiida/eos_wc.py deleted file mode 100644 index 37e18f0..0000000 --- a/eos/aiida/eos_wc.py +++ /dev/null @@ -1,74 +0,0 @@ -from aiida import orm - -from aiida import engine -from aiida_quantumespresso.workflows.pw.base import PwBaseWorkChain - -@engine.calcfunction -def rescale_list(structure: orm.StructureData, factor_list: orm.List): - - scaled_structure_dict = {} - - for index, scaling_factor in enumerate(factor_list.get_list()): - - ase_structure = structure.get_ase() - - new_cell = ase_structure.get_cell() * scaling_factor - ase_structure.set_cell(new_cell, scale_atoms=True) - - scaled_structure_dict[f'structure_{index}'] = orm.StructureData(ase=ase_structure) - - return scaled_structure_dict - - -@engine.calcfunction -def create_eos_dictionary(**kwargs) -> orm.Dict: - eos = { - label: (result['volume'], result['energy'], result['energy_units']) - for label, result in kwargs.items() - } - return orm.Dict(eos) - - -class EquationOfState(engine.WorkChain): - """WorkChain to compute Equation of State using Quantum ESPRESSO.""" - - @classmethod - def define(cls, spec): - """Specify inputs and outputs.""" - super().define(spec) - spec.input("code", valid_type=orm.Code) - spec.input("structure", valid_type=orm.StructureData) - spec.input("scale_factors", valid_type=orm.List) - - spec.outline( - cls.run_eos, - cls.results, - ) - spec.output("eos_dict", valid_type=orm.Dict) - - def run_eos(self): - - calcjob_dict = {} - - for label, rescaled_structure in rescale_list(self.inputs.structure, self.inputs.scale_factors).items(): - - builder = PwBaseWorkChain.get_builder_from_protocol( - code=self.inputs.code, - structure=rescaled_structure, - protocol='fast' - ) - calcjob_dict[label] = self.submit(builder) - - self.ctx.labels = list(calcjob_dict.keys()) - - return calcjob_dict - - def results(self): - - self.report(self.ctx) - - eos_results = { - label: self.ctx[label].outputs['output_parameters'] for label in self.ctx.labels - } - eos_dict = create_eos_dictionary(**eos_results) - self.out('eos_dict', eos_dict) diff --git a/eos/aiida/ev_curve.ipynb b/eos/aiida/ev_curve.ipynb deleted file mode 100644 index 3b5bc74..0000000 --- a/eos/aiida/ev_curve.ipynb +++ /dev/null @@ -1,795 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Equation of State curve\n", - "\n", - "> [MBx] The following notebook assumes that AiiDA has been installed and a profile has been configured with the required computers/codes. I'll work on a solution that uses a temporary profile in the future." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Profile" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from aiida import orm, engine, load_profile\n", - "\n", - "load_profile()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing a structure" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from pymatgen.core import Structure\n", - "\n", - "structure = orm.StructureData(\n", - " pymatgen_structure=Structure.from_file(\"../Si.cif\")\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Relaxing the geometry" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "11/19/2023 11:05:58 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4158|PwRelaxWorkChain|setup]: No change in volume possible for the provided base input parameters. Meta convergence is turned off.\n", - "11/19/2023 11:05:58 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4158|PwRelaxWorkChain|run_relax]: launching PwBaseWorkChain<4161>\n", - "11/19/2023 11:05:59 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4161|PwBaseWorkChain|run_process]: launching PwCalculation<4166> iteration #1\n", - "11/19/2023 11:05:59 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4161|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:06:00 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4161|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:06:00 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4158|PwRelaxWorkChain|inspect_relax]: after iteration 1 cell volume of relaxed structure is 40.888292209588094\n", - "11/19/2023 11:06:00 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4158|PwRelaxWorkChain|run_final_scf]: launching PwBaseWorkChain<4175> for final scf\n", - "11/19/2023 11:06:01 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4175|PwBaseWorkChain|run_process]: launching PwCalculation<4180> iteration #1\n", - "11/19/2023 11:06:01 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4175|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:06:01 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4175|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:06:01 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4158|PwRelaxWorkChain|results]: workchain completed after 1 iterations\n", - "11/19/2023 11:06:01 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4158|PwRelaxWorkChain|on_terminated]: remote folders will not be cleaned\n" - ] - } - ], - "source": [ - "from aiida_quantumespresso.workflows.pw.relax import PwRelaxWorkChain\n", - "from aiida_quantumespresso.common.types import RelaxType\n", - "\n", - "results = engine.run(PwRelaxWorkChain.get_builder_from_protocol(\n", - " code=orm.load_code('qe-7.2-pw@localhost'),\n", - " structure=structure,\n", - " relax_type=RelaxType.POSITIONS\n", - "))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "relaxed_structure = results['output_structure']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calc function to rescale structures\n", - "\n", - "The `calcfunction` below takes an input structure and rescales it to different volumes." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "@engine.calcfunction\n", - "def rescale_list(structure: orm.StructureData, factor_list: orm.List):\n", - "\n", - " scaled_structure_dict = {}\n", - "\n", - " for index, scaling_factor in enumerate(factor_list.get_list()):\n", - "\n", - " ase_structure = structure.get_ase()\n", - "\n", - " new_cell = ase_structure.get_cell() * scaling_factor\n", - " ase_structure.set_cell(new_cell, scale_atoms=True)\n", - "\n", - " scaled_structure_dict[f'structure_{index}'] = orm.StructureData(ase=ase_structure)\n", - "\n", - " return scaled_structure_dict" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Typically, you'd just run it by calling the function as you would a regular Python function:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "rescaled_structures = rescale_list(relaxed_structure, orm.List(list=[0.9, 0.95, 1.0, 1.05, 1.1]))" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'structure_0': ,\n", - " 'structure_1': ,\n", - " 'structure_2': ,\n", - " 'structure_3': ,\n", - " 'structure_4': }" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rescaled_structures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## EOS: Work function version" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from aiida_quantumespresso.workflows.pw.base import PwBaseWorkChain\n", - "\n", - "@engine.workfunction\n", - "def run_eos_wf(code: orm.Code, structure: orm.StructureData, scale_factors: orm.List):\n", - " \"\"\"Run an equation of state of a bulk crystal structure for the given element.\"\"\"\n", - "\n", - " parameters = {}\n", - "\n", - " for label, rescaled_structure in rescale_list(structure, scale_factors).items():\n", - "\n", - " builder = PwBaseWorkChain.get_builder_from_protocol(\n", - " code=code,\n", - " structure=rescaled_structure,\n", - " protocol='fast'\n", - " )\n", - " results = engine.run(builder)\n", - " parameters[label] = results['output_parameters']\n", - "\n", - " return parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "11/19/2023 11:12:43 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4206|PwBaseWorkChain|run_process]: launching PwCalculation<4211> iteration #1\n", - "11/19/2023 11:12:48 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4206|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:12:48 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4206|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:12:48 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4222|PwBaseWorkChain|run_process]: launching PwCalculation<4227> iteration #1\n", - "11/19/2023 11:12:54 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4222|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:12:54 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4222|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:12:54 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4238|PwBaseWorkChain|run_process]: launching PwCalculation<4243> iteration #1\n", - "11/19/2023 11:12:59 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4238|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:12:59 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4238|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:12:59 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4254|PwBaseWorkChain|run_process]: launching PwCalculation<4259> iteration #1\n", - "11/19/2023 11:13:04 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4254|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:13:04 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4254|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:13:04 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4270|PwBaseWorkChain|run_process]: launching PwCalculation<4275> iteration #1\n", - "11/19/2023 11:13:09 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4270|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:13:09 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4270|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n" - ] - } - ], - "source": [ - "results = run_eos_wf(\n", - " code=orm.load_code('qe-7.2-pw@localhost'),\n", - " structure=relaxed_structure,\n", - " scale_factors=[0.9, 0.95, 1.0, 1.05, 1.1]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'structure_0': ,\n", - " 'structure_1': ,\n", - " 'structure_2': ,\n", - " 'structure_3': ,\n", - " 'structure_4': }" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "volumes = []\n", - "energies = []\n", - "\n", - "for result in results.values():\n", - " volumes.append(result['volume'])\n", - " energies.append(result['energy'])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.plot(volumes, energies)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "@engine.calcfunction\n", - "def create_eos_dictionary(**kwargs) -> orm.Dict:\n", - " eos = {\n", - " label: (result['volume'], result['energy'], result['energy_units'])\n", - " for label, result in kwargs.items()\n", - " }\n", - " return orm.Dict(eos)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'structure_0': [29.80756462995, -307.18014869427, 'eV'],\n", - " 'structure_1': [35.056599073847, -308.2321719355, 'eV'],\n", - " 'structure_2': [40.888291673923, -308.34305832306, 'eV'],\n", - " 'structure_3': [47.333308649125, -308.08280198495, 'eV'],\n", - " 'structure_4': [54.422316216932, -307.51069734205, 'eV']}" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "create_eos_dictionary(**results).get_dict()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Work chain version" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "class EquationOfState(engine.WorkChain):\n", - " \"\"\"WorkChain to compute Equation of State using Quantum ESPRESSO.\"\"\"\n", - "\n", - " @classmethod\n", - " def define(cls, spec):\n", - " \"\"\"Specify inputs and outputs.\"\"\"\n", - " super().define(spec)\n", - " spec.input(\"code\", valid_type=orm.Code)\n", - " spec.input(\"structure\", valid_type=orm.StructureData)\n", - " spec.input(\"scale_factors\", valid_type=orm.List)\n", - "\n", - " spec.outline(\n", - " cls.run_eos,\n", - " cls.results,\n", - " )\n", - " spec.output(\"eos_dict\", valid_type=orm.Dict)\n", - "\n", - " def run_eos(self):\n", - "\n", - " calcjob_dict = {}\n", - "\n", - " for label, rescaled_structure in rescale_list(self.inputs.structure, self.inputs.scale_factors).items():\n", - "\n", - " builder = PwBaseWorkChain.get_builder_from_protocol(\n", - " code=self.inputs.code,\n", - " structure=rescaled_structure,\n", - " protocol='fast'\n", - " )\n", - " calcjob_dict[label] = self.submit(builder)\n", - "\n", - " self.ctx.labels = list(calcjob_dict.keys())\n", - "\n", - " # self.to_context(**calcjob_dict)\n", - " return calcjob_dict\n", - "\n", - " def results(self):\n", - "\n", - " self.report(self.ctx)\n", - "\n", - " eos_results = {\n", - " label: self.ctx[label].outputs['output_parameters'] for label in self.ctx.labels\n", - " }\n", - " eos_dict = create_eos_dictionary(**eos_results)\n", - " self.out('eos_dict', eos_dict)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "11/19/2023 11:14:58 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4320|PwBaseWorkChain|run_process]: launching PwCalculation<4333> iteration #1\n", - "11/19/2023 11:14:58 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4314|PwBaseWorkChain|run_process]: launching PwCalculation<4341> iteration #1\n", - "11/19/2023 11:14:59 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4308|PwBaseWorkChain|run_process]: launching PwCalculation<4349> iteration #1\n", - "11/19/2023 11:14:59 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4302|PwBaseWorkChain|run_process]: launching PwCalculation<4357> iteration #1\n", - "11/19/2023 11:15:00 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4296|PwBaseWorkChain|run_process]: launching PwCalculation<4365> iteration #1\n", - "11/19/2023 11:15:00 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4320|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:15:00 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4320|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:15:00 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4314|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:15:00 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4314|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:15:01 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4308|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:15:01 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4308|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:15:01 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4302|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:15:01 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4302|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:15:01 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4296|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:15:01 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4296|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:15:01 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4284|EquationOfState|results]: AttributeDict({'labels': ['structure_0', 'structure_1', 'structure_2', 'structure_3', 'structure_4'], 'structure_0': , 'structure_1': , 'structure_2': , 'structure_3': , 'structure_4': })\n" - ] - }, - { - "data": { - "text/plain": [ - "{'eos_dict': }" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "engine.run(EquationOfState, code=orm.load_code('qe-7.2-pw@localhost'),\n", - " structure=relaxed_structure,\n", - " scale_factors=orm.List([0.9, 0.95, 1.0, 1.05, 1.1]))" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "builder = EquationOfState.get_builder()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "builder.structure = relaxed_structure" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Process class: EquationOfState\n", - "Inputs:\n", - "metadata: {}\n", - "structure: Si\n" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "builder" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "builder.scale_factors = orm.List([0.9, 0.95, 1.0, 1.05, 1.1])\n", - "builder.code = orm.load_code('qe-7.2-pw@localhost')" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "11/19/2023 11:15:20 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4410|PwBaseWorkChain|run_process]: launching PwCalculation<4423> iteration #1\n", - "11/19/2023 11:15:21 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4404|PwBaseWorkChain|run_process]: launching PwCalculation<4431> iteration #1\n", - "11/19/2023 11:15:22 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4398|PwBaseWorkChain|run_process]: launching PwCalculation<4439> iteration #1\n", - "11/19/2023 11:15:22 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4392|PwBaseWorkChain|run_process]: launching PwCalculation<4447> iteration #1\n", - "11/19/2023 11:15:23 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4386|PwBaseWorkChain|run_process]: launching PwCalculation<4455> iteration #1\n", - "11/19/2023 11:15:23 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4410|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:15:23 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4410|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:15:24 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4404|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:15:24 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4404|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:15:24 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4398|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:15:24 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4398|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:15:24 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4392|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:15:24 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4392|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:15:24 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4386|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:15:25 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4386|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:15:25 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4374|EquationOfState|results]: AttributeDict({'labels': ['structure_0', 'structure_1', 'structure_2', 'structure_3', 'structure_4'], 'structure_0': , 'structure_1': , 'structure_2': , 'structure_3': , 'structure_4': })\n" - ] - } - ], - "source": [ - "results, node = engine.run_get_node(builder)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'structure_0': [29.80756462995, -307.18014869427, 'eV'],\n", - " 'structure_1': [35.056599073847, -308.2321719355, 'eV'],\n", - " 'structure_2': [40.888291673923, -308.34305832306, 'eV'],\n", - " 'structure_3': [47.333308649125, -308.08280198495, 'eV'],\n", - " 'structure_4': [54.422316216932, -307.51069734205, 'eV']}" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results['eos_dict'].get_dict()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "eos = node.outputs.eos_dict.get_dict()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'structure_0': [29.80756462995, -307.18014869427, 'eV'],\n", - " 'structure_1': [35.056599073847, -308.2321719355, 'eV'],\n", - " 'structure_2': [40.888291673923, -308.34305832306, 'eV'],\n", - " 'structure_3': [47.333308649125, -308.08280198495, 'eV'],\n", - " 'structure_4': [54.422316216932, -307.51069734205, 'eV']}" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "eos" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(\n", - " [v[0] for v in eos.values()],\n", - " [v[1] for v in eos.values()],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Restarting the work chain" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "restart_builder = node.get_builder_restart()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Process class: EquationOfState\n", - "Inputs:\n", - "code: Plane wave Quantum ESPRESSO v7.2 - local compilation\n", - "metadata: {}\n", - "scale_factors: ''\n", - "structure: Si\n" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "restart_builder" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "11/19/2023 11:15:45 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4499|PwBaseWorkChain|run_process]: launching PwCalculation<4512> iteration #1\n", - "11/19/2023 11:15:46 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4493|PwBaseWorkChain|run_process]: launching PwCalculation<4520> iteration #1\n", - "11/19/2023 11:15:46 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4487|PwBaseWorkChain|run_process]: launching PwCalculation<4528> iteration #1\n", - "11/19/2023 11:15:47 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4481|PwBaseWorkChain|run_process]: launching PwCalculation<4536> iteration #1\n", - "11/19/2023 11:15:47 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4475|PwBaseWorkChain|run_process]: launching PwCalculation<4544> iteration #1\n", - "11/19/2023 11:15:48 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4499|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:15:48 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4499|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:15:48 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4493|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:15:48 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4493|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:15:48 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4487|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:15:48 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4487|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:15:48 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4481|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:15:49 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4481|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:15:49 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4475|PwBaseWorkChain|results]: work chain completed after 1 iterations\n", - "11/19/2023 11:15:49 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4475|PwBaseWorkChain|on_terminated]: remote folders will not be cleaned\n", - "11/19/2023 11:15:49 AM <38951> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [4463|EquationOfState|results]: AttributeDict({'labels': ['structure_0', 'structure_1', 'structure_2', 'structure_3', 'structure_4'], 'structure_0': , 'structure_1': , 'structure_2': , 'structure_3': , 'structure_4': })\n" - ] - }, - { - "data": { - "text/plain": [ - "{'eos_dict': }" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "engine.run(restart_builder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submitting to the daemon\n", - "\n", - "> Note: this requires that the daemon knows where to find the work chain, which means the work chain is stored in a module that is in the `PYTHONPATH`, or (preferably) it is developed in an installed package." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "from aiida import engine\n", - "from eos_wc import EquationOfState\n", - "\n", - "builder = EquationOfState.get_builder()\n", - "\n", - "builder.structure = relaxed_structure\n", - "builder.scale_factors = orm.List([0.9, 0.95, 1.0, 1.05, 1.1])\n", - "builder.code = orm.load_code('qe-7.2-pw@localhost')" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "engine.submit(builder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `WorkChain` approach is not very suitable for interactively building a workflow. We've been working on a more flexible variant: the `WorkTree` (I know, we like workflows so much we can't get enough of them):\n", - "\n", - "https://aiida-worktree.readthedocs.io/en/stable/" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "qe-installed", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/eos/aiida/ev_curve_basic.ipynb b/eos/aiida/ev_curve_basic.ipynb deleted file mode 100644 index da0b377..0000000 --- a/eos/aiida/ev_curve_basic.ipynb +++ /dev/null @@ -1,751 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Equation of State curve - basic QE\n", - "\n", - "Running an EOS without all the fancy features in the `aiida-quantumespresso` plugin." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Profile" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pathlib import Path\n", - "\n", - "from aiida import orm, engine, load_profile\n", - "\n", - "load_profile()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing a structure" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from ase import io as ase_io\n", - "\n", - "structure = orm.StructureData(ase=ase_io.read('files/Al.cif'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Relaxing the geometry" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "resources = {\n", - " 'num_machines': 1,\n", - " 'num_mpiprocs_per_machine': 1\n", - "}\n", - "\n", - "relax_params = {\n", - " 'CONTROL': {\n", - " 'calculation': 'vc-relax',\n", - " 'pseudo_dir': Path('files').absolute().as_posix(),\n", - " },\n", - " 'SYSTEM': {\n", - " 'occupations': 'smearing',\n", - " 'smearing': 'cold',\n", - " 'degauss': 0.02\n", - " }\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from aiida_qe_basic.pw import PwCalculation\n", - "\n", - "builder = PwCalculation.get_builder()\n", - "\n", - "builder.code = orm.load_code('pw@localhost')\n", - "builder.structure = orm.StructureData(ase=ase_io.read('files/Al.cif'))\n", - "builder.pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\n", - "builder.parameters = orm.Dict(relax_params)\n", - "builder.metadata.options.resources = resources" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results = engine.run(builder)\n", - "relaxed_structure = results['structure']\n", - "relaxed_structure" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calc function to rescale structures\n", - "\n", - "The `calcfunction` below takes an input structure and rescales it to different volumes." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from aiida_qe_basic.pw import PwCalculation\n", - "\n", - "@engine.calcfunction\n", - "def rescale_list(structure: orm.StructureData, factor_list: orm.List):\n", - "\n", - " scaled_structure_dict = {}\n", - "\n", - " for index, scaling_factor in enumerate(factor_list.get_list()):\n", - "\n", - " ase_structure = structure.get_ase()\n", - "\n", - " new_cell = ase_structure.get_cell() * scaling_factor\n", - " ase_structure.set_cell(new_cell, scale_atoms=True)\n", - "\n", - " scaled_structure_dict[f'structure_{index}'] = orm.StructureData(ase=ase_structure)\n", - "\n", - " return scaled_structure_dict" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Typically, you'd just run it by calling the function as you would a regular Python function:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "rescaled_structures = rescale_list(relaxed_structure, orm.List(list=[0.9, 0.95, 1.0, 1.05, 1.1]))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'structure_0': ,\n", - " 'structure_1': ,\n", - " 'structure_2': ,\n", - " 'structure_3': ,\n", - " 'structure_4': }" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rescaled_structures" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## EOS: Work function version" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "scf_inputs = {\n", - " 'CONTROL': {\n", - " 'calculation': 'scf',\n", - " 'pseudo_dir': Path('files').absolute().as_posix(),\n", - " },\n", - " 'SYSTEM': {\n", - " 'occupations': 'smearing',\n", - " 'smearing': 'cold',\n", - " 'degauss': 0.02\n", - " }\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "@engine.workfunction\n", - "def run_eos_wf(code: orm.Code, structure: orm.StructureData, scale_factors: orm.List):\n", - " \"\"\"Run an equation of state of a bulk crystal structure for the given element.\"\"\"\n", - "\n", - " properties = {}\n", - "\n", - " for label, rescaled_structure in rescale_list(structure, scale_factors).items():\n", - "\n", - " builder = PwCalculation.get_builder()\n", - " builder.code = code\n", - " builder.structure = rescaled_structure\n", - " builder.parameters = orm.Dict(scf_inputs)\n", - " builder.pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\n", - " builder.metadata.options.resources = resources\n", - "\n", - " results = engine.run(builder)\n", - " properties[label] = results['properties']\n", - "\n", - " return properties" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "results = run_eos_wf(\n", - " code=orm.load_code('pw@localhost'),\n", - " structure=relaxed_structure,\n", - " scale_factors=[0.9, 0.95, 1.0, 1.05, 1.1]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'structure_0': ,\n", - " 'structure_1': ,\n", - " 'structure_2': ,\n", - " 'structure_3': ,\n", - " 'structure_4': }" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "volumes = []\n", - "energies = []\n", - "\n", - "for result in results.values():\n", - " volumes.append(result['volume'])\n", - " energies.append(result['energy'])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.plot(volumes, energies)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Work chain version" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "@engine.calcfunction\n", - "def create_eos_dictionary(**kwargs) -> orm.Dict:\n", - " eos = {\n", - " label: (result['volume'], result['energy'])\n", - " for label, result in kwargs.items()\n", - " }\n", - " return orm.Dict(eos)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'structure_0': [11.863200733459, -536.3535586307],\n", - " 'structure_1': [13.952279463442, -536.79015879477],\n", - " 'structure_2': [16.273252034923, -536.90415317779],\n", - " 'structure_3': [18.838323386928, -536.82149044905],\n", - " 'structure_4': [21.659698458483, -536.62164959218]}" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "create_eos_dictionary(**results).get_dict()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "class EquationOfState(engine.WorkChain):\n", - " \"\"\"WorkChain to compute Equation of State using Quantum ESPRESSO.\"\"\"\n", - "\n", - " @classmethod\n", - " def define(cls, spec):\n", - " \"\"\"Specify inputs and outputs.\"\"\"\n", - " super().define(spec)\n", - " spec.input(\"code\", valid_type=orm.Code)\n", - " spec.input(\"structure\", valid_type=orm.StructureData)\n", - " spec.input(\"scale_factors\", valid_type=orm.List)\n", - "\n", - " spec.outline(\n", - " cls.run_eos,\n", - " cls.results,\n", - " )\n", - " spec.output(\"eos_dict\", valid_type=orm.Dict)\n", - "\n", - " def run_eos(self):\n", - "\n", - " calcjob_dict = {}\n", - "\n", - " for label, rescaled_structure in rescale_list(self.inputs.structure, self.inputs.scale_factors).items():\n", - "\n", - " builder = PwCalculation.get_builder()\n", - " builder.code = self.inputs.code\n", - " builder.structure = rescaled_structure\n", - " builder.parameters = orm.Dict(scf_inputs)\n", - " builder.pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\n", - " builder.metadata.options.resources = resources\n", - "\n", - " calcjob_dict[label] = self.submit(builder)\n", - "\n", - " self.ctx.labels = list(calcjob_dict.keys())\n", - "\n", - " return calcjob_dict\n", - "\n", - " def results(self):\n", - "\n", - " self.report(self.ctx)\n", - "\n", - " eos_results = {\n", - " label: self.ctx[label].outputs['properties'] for label in self.ctx.labels\n", - " }\n", - " eos_dict = create_eos_dictionary(**eos_results)\n", - " self.out('eos_dict', eos_dict)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "01/11/2024 03:08:58 PM <26503> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [79|EquationOfState|results]: AttributeDict({'labels': ['structure_0', 'structure_1', 'structure_2', 'structure_3', 'structure_4'], 'structure_0': , 'structure_1': , 'structure_2': , 'structure_3': , 'structure_4': })\n" - ] - }, - { - "data": { - "text/plain": [ - "{'eos_dict': }" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "engine.run(EquationOfState, code=orm.load_code('pw@localhost'),\n", - " structure=relaxed_structure,\n", - " scale_factors=orm.List([0.9, 0.95, 1.0, 1.05, 1.1]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using the `builder`" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "builder = EquationOfState.get_builder()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "builder.structure = relaxed_structure" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Process class: EquationOfState\n", - "Inputs:\n", - "metadata: {}\n", - "structure: Al\n" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "builder" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "builder.scale_factors = orm.List([0.9, 0.95, 1.0, 1.05, 1.1])\n", - "builder.code = orm.load_code('pw@localhost')" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "01/11/2024 03:09:24 PM <26503> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [124|EquationOfState|results]: AttributeDict({'labels': ['structure_0', 'structure_1', 'structure_2', 'structure_3', 'structure_4'], 'structure_0': , 'structure_1': , 'structure_2': , 'structure_3': , 'structure_4': })\n" - ] - } - ], - "source": [ - "results, node = engine.run_get_node(builder)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'structure_0': [11.863200731903, -536.35355863018],\n", - " 'structure_1': [13.952279461612, -536.79015879553],\n", - " 'structure_2': [16.273252032788, -536.90415317779],\n", - " 'structure_3': [18.838323384456, -536.8214904491],\n", - " 'structure_4': [21.65969845564, -536.62164959241]}" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results['eos_dict'].get_dict()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "eos = node.outputs.eos_dict.get_dict()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'structure_0': [11.863200733459, -536.3535586307],\n", - " 'structure_1': [13.952279463442, -536.79015879477],\n", - " 'structure_2': [16.273252034923, -536.90415317779],\n", - " 'structure_3': [18.838323386928, -536.82149044905],\n", - " 'structure_4': [21.659698458483, -536.62164959218]}" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "eos" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(\n", - " [v[0] for v in eos.values()],\n", - " [v[1] for v in eos.values()],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Restarting the work chain" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "restart_builder = node.get_builder_restart()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "01/11/2024 03:10:16 PM <26503> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [168|EquationOfState|results]: AttributeDict({'labels': ['structure_0', 'structure_1', 'structure_2', 'structure_3', 'structure_4'], 'structure_0': , 'structure_1': , 'structure_2': , 'structure_3': , 'structure_4': })\n" - ] - }, - { - "data": { - "text/plain": [ - "{'eos_dict': }" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "engine.run(restart_builder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Submitting to the daemon\n", - "\n", - "> Note: this requires that the daemon knows where to find the work chain, which means the work chain is stored in a module that is in the `PYTHONPATH`, or (preferably) it is developed in an installed package." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "from aiida import engine\n", - "from eos_wc import EquationOfState\n", - "\n", - "builder = EquationOfState.get_builder()\n", - "\n", - "builder.structure = relaxed_structure\n", - "builder.scale_factors = orm.List([0.9, 0.95, 1.0, 1.05, 1.1])\n", - "builder.code = orm.load_code('qe-7.2-pw@localhost')" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "engine.submit(builder)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `WorkChain` approach is not very suitable for interactively building a workflow. We've been working on a more flexible variant: the `WorkTree` (I know, we like workflows so much we can't get enough of them):\n", - "\n", - "https://aiida-worktree.readthedocs.io/en/stable/" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "qe-installed", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/eos/aiida/files/Al.cif b/eos/aiida/files/Al.cif deleted file mode 100644 index 1bc4e6b..0000000 --- a/eos/aiida/files/Al.cif +++ /dev/null @@ -1,33 +0,0 @@ - -########################################################################## -# Crystallographic Information Format file -# Produced by PyCifRW module -# -# This is a CIF file. CIF has been adopted by the International -# Union of Crystallography as the standard for data archiving and -# transmission. -# -# For information on this file format, follow the CIF links at -# http://www.iucr.org -########################################################################## - -data_0 - -loop_ - _atom_site_label - _atom_site_fract_x - _atom_site_fract_y - _atom_site_fract_z - _atom_site_type_symbol - Al1 0.0 0.0 0.0 Al -_cell_angle_alpha 60.00000000000001 -_cell_angle_beta 60.00000000000001 -_cell_angle_gamma 60.00000000000001 -_cell_length_a 2.8569378254442808 -_cell_length_b 2.8569378254442808 -_cell_length_c 2.8569378254442808 -loop_ - _symmetry_equiv_pos_as_xyz - 'x, y, z' -_symmetry_int_tables_number 1 -_symmetry_space_group_name_H-M 'P 1' \ No newline at end of file diff --git a/eos/aiida/qe-plugin-inputs/eos-scf/aiida.in b/eos/aiida/qe-plugin-inputs/eos-scf/aiida.in deleted file mode 100644 index 13925b3..0000000 --- a/eos/aiida/qe-plugin-inputs/eos-scf/aiida.in +++ /dev/null @@ -1,39 +0,0 @@ -&CONTROL - calculation = 'scf' - etot_conv_thr = 2.0000000000d-05 - forc_conv_thr = 1.0000000000d-04 - max_seconds = 3.4200000000d+03 - outdir = './out/' - prefix = 'aiida' - pseudo_dir = './pseudo/' - tprnfor = .true. - tstress = .true. - verbosity = 'high' -/ -&SYSTEM - degauss = 1.0000000000d-02 - ecutrho = 2.4000000000d+02 - ecutwfc = 3.0000000000d+01 - ibrav = 0 - nat = 2 - nosym = .false. - ntyp = 1 - occupations = 'smearing' - smearing = 'cold' -/ -&ELECTRONS - conv_thr = 4.0000000000d-10 - electron_maxstep = 80 - mixing_beta = 4.0000000000d-01 -/ -ATOMIC_SPECIES -Si 28.0855 Si.pbesol-n-rrkjus_psl.1.0.0.UPF -ATOMIC_POSITIONS angstrom -Si 3.0140084413 2.1312258074 5.2204157547 -Si 2.0093389608 1.4208172049 3.4802771698 -K_POINTS automatic -15 15 15 0 0 0 -CELL_PARAMETERS angstrom - 3.0140084413 0.0000000000 1.7401385849 - 1.0046694804 2.8416344098 1.7401385849 - 0.0000000000 0.0000000000 3.4802771698 diff --git a/eos/aiida/qe-plugin-inputs/eos-scf/pseudo/Si.pbesol-n-rrkjus_psl.1.0.0.UPF b/eos/aiida/qe-plugin-inputs/eos-scf/pseudo/Si.pbesol-n-rrkjus_psl.1.0.0.UPF deleted file mode 100644 index b1d7c0c..0000000 --- a/eos/aiida/qe-plugin-inputs/eos-scf/pseudo/Si.pbesol-n-rrkjus_psl.1.0.0.UPF +++ /dev/null @@ -1,16563 +0,0 @@ - - - Generated using "atomic" code by A. Dal Corso v.6.0 svn rev. 13079 - Author: ADC - Generation date: 15Feb2019 - Pseudopotential type: USPP - Element: Si - Functional: PBESOL - - Suggested minimum cutoff for wavefunctions: 44. Ry - Suggested minimum cutoff for charge density: 175. Ry - The Pseudo was generated with a Scalar-Relativistic Calculation - Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 1.9000 - Pseudopotential contains additional information for GIPAW reconstruction. - - Valence configuration: - nl pn l occ Rcut Rcut US E pseu - 3S 1 0 2.00 1.600 1.800 -0.791473 - 3P 2 1 2.00 1.600 1.800 -0.298190 - Generation configuration: - 3S 1 0 2.00 1.600 1.800 -0.791470 - 3S 1 0 0.00 1.600 1.800 6.000000 - 3P 2 1 2.00 1.600 1.800 -0.298190 - 3P 2 1 0.00 1.600 1.800 6.000000 - 3D 3 2 0.00 1.600 1.800 0.100000 - 3D 3 2 0.00 1.600 1.800 0.300000 - - Pseudization used: troullier-martins - - &input - title='Si', - zed=14., - rel=1, - config='[Ne] 3s2 3p2 3d-1', - iswitch=3, - dft='PBESOL' - / - &inputp - lpaw=.false., - pseudotype=3, - file_pseudopw='Si.pbesol-n-rrkjus_psl.1.0.0.UPF', - author='ADC', - lloc=-1, - rcloc=1.9, - which_augfun='PSQ', - rmatch_augfun_nc=.true., - nlcc=.true., - new_core_ps=.true., - rcore=1.3, - tm=.true. - / 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-3.944662096506125E+000 -3.911686378014416E+000 --3.877876762602193E+000 -3.843288238110802E+000 -3.807978152841701E+000 -3.772005950479453E+000 --3.735432862057115E+000 -3.698321552464791E+000 -3.660735718542186E+000 -3.622739636326716E+000 --3.584397656065700E+000 -3.545773642520868E+000 -3.506930358290723E+000 -3.467928789027743E+000 --3.428827409455470E+000 -3.389681389479795E+000 -3.350541740250047E+000 -3.311454400691617E+000 --3.272459265643767E+000 -3.233589157451999E+000 -3.194868743935744E+000 -3.156312019121804E+000 --3.117922772286063E+000 -3.079704324848941E+000 -3.041660978408694E+000 -3.003797955721384E+000 --2.966121309355073E+000 -2.928637813960342E+000 -2.891354846966412E+000 -2.854280252819465E+000 --2.817422208045965E+000 -2.780789092657058E+000 -2.744389363280581E+000 -2.708231432587880E+000 --2.672323556218584E+000 -2.636673727892762E+000 -2.601289583028112E+000 -2.566178310886387E+000 --2.531346575066245E+000 -2.496800442021367E+000 -2.462545317205477E+000 -2.428585888414538E+000 --2.394926075902772E+000 -2.361568988889872E+000 -2.328516888140261E+000 -2.295771154383303E+000 --2.263332262452322E+000 -2.231199761141912E+000 -2.199372258922316E+000 -2.167847415390080E+000 --2.136621940956548E+000 -2.105691602493944E+000 -2.075051234825354E+000 -2.044694765556703E+000 --2.014615248988357E+000 -1.984804909724494E+000 -1.955255201039407E+000 -1.925956877641751E+000 --1.896900084840394E+000 -1.868074465741824E+000 -1.839469287993581E+000 -1.811073591346752E+000 --1.782876356936021E+000 -1.754866698650148E+000 -1.727034076266229E+000 -1.699368529151118E+000 --1.671860928310442E+000 -1.644503243392565E+000 -1.617288821092236E+000 -1.590212663192413E+000 --1.563271705714333E+000 -1.536465092482912E+000 -1.509794415296824E+000 -1.483263927393269E+000 --1.456880721065133E+000 -1.430654849424465E+000 -1.404599389186376E+000 -1.378730435791939E+000 --1.353067025281141E+000 -1.327630979777946E+000 -1.302446676241165E+000 -1.277540741099616E+000 --1.252941676344987E+000 -1.228679425416713E+000 -1.204784889596442E+000 -1.181289407489713E+000 --1.158224211402747E+000 -1.135619874966294E+000 -1.113505766218995E+000 -1.091909519589443E+000 --1.070856538903010E+000 -1.050369541813220E+000 -1.030468154059665E+000 -1.011168559830622E+000 --9.924832123987772E-001 -9.744206072151107E-001 -9.569851178870253E-001 -9.401768939948315E-001 --9.239918185450073E-001 -9.084215220371491E-001 -8.934534496055477E-001 -8.790709774587120E-001 --8.652535748321453E-001 -8.519770078216692E-001 -8.392135817295682E-001 -8.269324188550725E-001 --8.150997689402559E-001 -8.036793496931056E-001 -7.926327148912131E-001 -7.819196475101565E-001 --7.714985750805915E-001 -7.613270040683490E-001 -7.513619695057696E-001 -7.415604953964788E-001 --7.318800606386885E-001 -7.222790644051664E-001 -7.127172841609264E-001 -7.031563188704608E-001 --6.935600095079838E-001 -6.838948288191581E-001 -6.741302324273349E-001 -6.642389638735382E-001 --6.541973070333984E-001 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-1.119195563174703E-008 --1.001265196592323E-008 -9.000002889380742E-009 -8.123050794719028E-009 -7.357206946246456E-009 --6.683041803226522E-009 -6.084818110166622E-009 -4.592126150402380E-010 -3.764932334064363E-010 --3.141998672843246E-010 -2.676513523937142E-010 -2.329400697971417E-010 -2.071594333036061E-010 --1.880809260529840E-010 -1.739505551296987E-010 -1.635594460537431E-010 -1.558994864947119E-010 --1.502971457418183E-010 -1.461961574873885E-010 -1.431566164671041E-010 -1.409651969756874E-010 --1.365013585647812E-010 -1.355416026804930E-010 -1.348833755102176E-010 -1.344029873068487E-010 --1.341019414470874E-010 -1.338834436439654E-010 -1.337717958760957E-010 -1.337067841526740E-010 --1.336570122427598E-010 -1.336649526407843E-010 -1.336519459238951E-010 -1.336838136836424E-010 --1.337257300068445E-010 -1.337524089512985E-010 -1.337979642672787E-010 -1.338318074164370E-010 --1.338806472864370E-010 -1.339333804269968E-010 -1.339697063444319E-010 -1.340259112579444E-010 --1.340570301804555E-010 -1.341088990147230E-010 -1.341662391769872E-010 -1.341918732671851E-010 --1.342508213788510E-010 -1.342759068468332E-010 -1.343356435508498E-010 -1.343920391754634E-010 --1.344256686115914E-010 -1.344665780211262E-010 -1.344932578518539E-010 -1.345500451239001E-010 --1.346064944612067E-010 -1.346316368297111E-010 -1.346824457220862E-010 -1.347089668365117E-010 --1.347623500207347E-010 -1.348110678811985E-010 -1.348319408064432E-010 -1.348902957127663E-010 --1.349185709698767E-010 -1.349641413501032E-010 -1.350162772919074E-010 -1.350436576888593E-010 --1.350885468026711E-010 -1.351131989253342E-010 -1.351574696616284E-010 -1.352025360468161E-010 --1.352210801018680E-010 -1.352631540351615E-010 -1.352848920847621E-010 -1.353287515708813E-010 --1.353720900556444E-010 -1.353911016609996E-010 -1.354351431135027E-010 -1.354561331592798E-010 --1.354894937753295E-010 -1.355359513401354E-010 -1.355607194579387E-010 -1.356020870876676E-010 --1.356143031898045E-010 -1.356555453783390E-010 -1.356913110402091E-010 -1.357110525166373E-010 --1.357496558893492E-010 -1.357615531562246E-010 -1.357962558773646E-010 -1.358421641738582E-010 --1.358435826252601E-010 -1.358838869854047E-010 -1.358938040469699E-010 -1.359242757214595E-010 --1.359577180912569E-010 -1.359739251852002E-010 -1.360020144533820E-010 -1.360057810989465E-010 --1.360424151953737E-010 -1.360638596324416E-010 -1.360734446648956E-010 -1.361042480080479E-010 --1.361161683581274E-010 -1.361400138807014E-010 -1.361745811291156E-010 -1.361807071049209E-010 --1.362077373438175E-010 -1.362079255063152E-010 -1.362289255078295E-010 -1.362585180916210E-010 --1.362590289919942E-010 -1.362788292316286E-010 -1.362807496904526E-010 -1.362965841381380E-010 --1.363206055641100E-010 -1.363214653303214E-010 -1.363364449630159E-010 -1.363336187998931E-010 --1.363491485580384E-010 -1.363608561830143E-010 -1.363606593212774E-010 -1.363744767432582E-010 --1.363694699084751E-010 -1.363902037521223E-010 -1.364022011716266E-010 -1.363916738050561E-010 --1.363959269229359E-010 -1.363902173250950E-010 -1.364016325658151E-010 -1.363996231362885E-010 --1.363932426358416E-010 -1.363987318014537E-010 -1.363841406174945E-010 -1.363958482893633E-010 --1.363960428301297E-010 - - - - diff --git a/eos/aiida/qe-plugin-inputs/relax/aiida.in b/eos/aiida/qe-plugin-inputs/relax/aiida.in deleted file mode 100644 index 4ac2576..0000000 --- a/eos/aiida/qe-plugin-inputs/relax/aiida.in +++ /dev/null @@ -1,41 +0,0 @@ -&CONTROL - calculation = 'relax' - etot_conv_thr = 2.0000000000d-05 - forc_conv_thr = 1.0000000000d-04 - max_seconds = 3.4200000000d+03 - outdir = './out/' - prefix = 'aiida' - pseudo_dir = './pseudo/' - tprnfor = .true. - tstress = .true. - verbosity = 'high' -/ -&SYSTEM - degauss = 1.0000000000d-02 - ecutrho = 2.4000000000d+02 - ecutwfc = 3.0000000000d+01 - ibrav = 0 - nat = 2 - nosym = .false. - ntyp = 1 - occupations = 'smearing' - smearing = 'cold' -/ -&ELECTRONS - conv_thr = 4.0000000000d-10 - electron_maxstep = 80 - mixing_beta = 4.0000000000d-01 -/ -&IONS -/ -ATOMIC_SPECIES -Si 28.0855 Si.pbesol-n-rrkjus_psl.1.0.0.UPF -ATOMIC_POSITIONS angstrom -Si 3.3488982827 2.3680286852 5.8004619750 -Si 2.2325988551 1.5786857901 3.8669746500 -K_POINTS automatic -14 14 14 0 0 0 -CELL_PARAMETERS angstrom - 3.3488982827 0.0000000000 1.9334873250 - 1.1162994276 3.1573715803 1.9334873250 - 0.0000000000 0.0000000000 3.8669746500 diff --git a/eos/aiida/qe-plugin-inputs/relax/pseudo/Si.pbesol-n-rrkjus_psl.1.0.0.UPF b/eos/aiida/qe-plugin-inputs/relax/pseudo/Si.pbesol-n-rrkjus_psl.1.0.0.UPF deleted file mode 100644 index b1d7c0c..0000000 --- a/eos/aiida/qe-plugin-inputs/relax/pseudo/Si.pbesol-n-rrkjus_psl.1.0.0.UPF +++ /dev/null @@ -1,16563 +0,0 @@ - - - Generated using "atomic" code by A. Dal Corso v.6.0 svn rev. 13079 - Author: ADC - Generation date: 15Feb2019 - Pseudopotential type: USPP - Element: Si - Functional: PBESOL - - Suggested minimum cutoff for wavefunctions: 44. Ry - Suggested minimum cutoff for charge density: 175. Ry - The Pseudo was generated with a Scalar-Relativistic Calculation - Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 1.9000 - Pseudopotential contains additional information for GIPAW reconstruction. - - Valence configuration: - nl pn l occ Rcut Rcut US E pseu - 3S 1 0 2.00 1.600 1.800 -0.791473 - 3P 2 1 2.00 1.600 1.800 -0.298190 - Generation configuration: - 3S 1 0 2.00 1.600 1.800 -0.791470 - 3S 1 0 0.00 1.600 1.800 6.000000 - 3P 2 1 2.00 1.600 1.800 -0.298190 - 3P 2 1 0.00 1.600 1.800 6.000000 - 3D 3 2 0.00 1.600 1.800 0.100000 - 3D 3 2 0.00 1.600 1.800 0.300000 - - Pseudization used: troullier-martins - - &input - title='Si', - zed=14., - rel=1, - config='[Ne] 3s2 3p2 3d-1', - iswitch=3, - dft='PBESOL' - / - &inputp - lpaw=.false., - pseudotype=3, - file_pseudopw='Si.pbesol-n-rrkjus_psl.1.0.0.UPF', - author='ADC', - lloc=-1, - rcloc=1.9, - which_augfun='PSQ', - rmatch_augfun_nc=.true., - nlcc=.true., - new_core_ps=.true., - rcore=1.3, - tm=.true. - / -6 -3S 1 0 2.00 0.00 1.60 1.80 0.0 -3S 1 0 0.00 6.00 1.60 1.80 0.0 -3P 2 1 2.00 0.00 1.60 1.80 0.0 -3P 2 1 0.00 6.00 1.60 1.80 0.0 -3D 3 2 0.00 0.10 1.60 1.80 0.0 -3D 3 2 0.00 0.30 1.60 1.80 0.0 - - - - - - - - - 6.513442611103688E-005 6.595371633350159E-005 6.678331195832729E-005 6.762334261151813E-005 - 6.847393954957285E-005 6.933523567999349E-005 7.020736558205276E-005 7.109046552782224E-005 - 7.198467350346503E-005 7.289012923079676E-005 7.380697418911693E-005 7.473535163731561E-005 - 7.567540663625787E-005 7.662728607144977E-005 7.759113867598985E-005 7.856711505380845E-005 - 7.955536770320024E-005 8.055605104065232E-005 8.156932142497197E-005 8.259533718171841E-005 - 8.363425862794104E-005 8.468624809722948E-005 8.575146996507863E-005 8.683009067457247E-005 - 8.792227876239150E-005 8.902820488514639E-005 9.014804184604389E-005 9.128196462188746E-005 - 9.243015039041771E-005 9.359277855799715E-005 9.477003078764225E-005 9.596209102740905E-005 - 9.716914553913527E-005 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-1.364022011716266E-010 -1.363916738050561E-010 --1.363959269229359E-010 -1.363902173250950E-010 -1.364016325658151E-010 -1.363996231362885E-010 --1.363932426358416E-010 -1.363987318014537E-010 -1.363841406174945E-010 -1.363958482893633E-010 --1.363960428301297E-010 - - - - diff --git a/eos/aiida/qe-setup.ipynb b/eos/aiida/qe-setup.ipynb deleted file mode 100644 index 904ac25..0000000 --- a/eos/aiida/qe-setup.ipynb +++ /dev/null @@ -1,188 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Profile" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pathlib import Path\n", - "from ase import io as ase_io\n", - "\n", - "from aiida import orm, engine, load_profile\n", - "from aiida.common.exceptions import NotExistent\n", - "\n", - "load_profile()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Setup for running Quantum ESPRESSO\n", - "\n", - "The cells below will setup the `localhost` computer and `pw.x` code on this computer." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " localhost = orm.load_computer('localhost')\n", - "except NotExistent:\n", - " localhost = orm.Computer(\n", - " label='localhost',\n", - " hostname='localhost',\n", - " transport_type='core.local',\n", - " scheduler_type='core.direct',\n", - " workdir=Path('workdir').absolute().as_posix()\n", - " ).store()\n", - " localhost.configure()\n", - "\n", - "try:\n", - " pw_code = orm.load_code('pw@localhost')\n", - "except NotExistent:\n", - " pw_code = orm.InstalledCode(\n", - " label='pw',\n", - " computer=localhost,\n", - " filepath_executable='pw.x',\n", - " default_calc_job_plugin='aiida_qe_basic.pw',\n", - " prepend_text='export OMP_NUM_THREADS=1'\n", - " ).store()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the cell below you can set up a basic `pw.x` run via the plugin defined in the `aiida_qe_basic` package." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from aiida_qe_basic.pw import PwCalculation\n", - "\n", - "builder = PwCalculation.get_builder()\n", - "\n", - "builder.code = pw_code\n", - "builder.structure = orm.StructureData(ase=ase_io.read('files/Al.cif'))\n", - "builder.pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\n", - "builder.parameters = orm.Dict(\n", - " {\n", - " 'CONTROL': {\n", - " 'calculation': 'scf',\n", - " 'pseudo_dir': Path('files').absolute().as_posix(),\n", - " },\n", - " 'SYSTEM': {\n", - " 'occupations': 'smearing',\n", - " 'smearing': 'cold',\n", - " 'degauss': 0.02\n", - " }\n", - " }\n", - ")\n", - "builder.metadata.options.resources = {\n", - " 'num_machines': 1,\n", - " 'num_mpiprocs_per_machine': 1\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "results = engine.run(builder)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'structure': ,\n", - " 'properties': ,\n", - " 'remote_folder': ,\n", - " 'retrieved': }" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'energy': -536.90337599314, 'volume': 16.48873596666}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "results['properties'].get_dict()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/eos/atomate2/eos.png b/eos/atomate2/eos.png deleted file mode 100644 index 7b10d88bbcdabf81a2a375a55d1c39ffd2458e8d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 30680 zcmdqJWmHyM7dE_Uq`O-L1q4K-1q2iX1VjuFDQQF+3F#IkMMS!!MJY*1=@jX1l#oz5 zzPWjxH@-36_s{q9J7b)24zTZiuf5h>b6(dqueC!o)D(#c7zhvqA-=7opot)u-Uxy* zjgJezxz;y65C0K&y!puSo{g#F3qyMo+EbQ;T9PX`2m?W|{ zQdZeDF1}rlbQHalG{u_fdTwIdcszNf-6_sNEy+Pf{Q6=CeNW^$cI;sIr`5DIlSEbl z{<#*4!$wX{9{I))LkWH~e)?$&ot(QQLR zL$Q~8f)tdLUo85$;1dFbj*gCZZe8Fw_XXiB*3^)t**#b( ztj&G)8$uow%`vv?hC%5C!$U1xQM$^84PA1^5>$=2S!VJ=)I zOrZAYhjH9_#W1bqKNT6P7hD(P%z8f?%EKfXbiTh5uqCxzI;7{9o_;>bW%iuo>M;48 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bulk\n", - "from ase.io import write\n", - "from jobflow import job, Response, Flow, run_locally, Maker\n", - "from pymatgen.core import Structure\n", - "from pymatgen.io.ase import AseAtomsAdaptor" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-01-19T22:49:01.782036371Z", - "start_time": "2024-01-19T22:48:55.903556288Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "pseudopotentials = {\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"}\n", - "pseudo_dir = str(Path.cwd() / 'pseudo')" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-01-19T22:49:01.785090234Z", - "start_time": "2024-01-19T22:49:01.782580256Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "parameters_relax = {\n", - " 'CONTROL': {\n", - " 'calculation': 'vc-relax',\n", - " 'pseudo_dir': pseudo_dir,\n", - " },\n", - " 'SYSTEM': {\n", - " 'occupations': 'smearing',\n", - " 'degauss': 0.02,\n", - " }\n", - "}" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-01-19T22:49:01.793593996Z", - "start_time": "2024-01-19T22:49:01.787909824Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [], - "source": [ - "parameters_static = {\n", - " 'CONTROL': {\n", - " 'calculation': 'scf',\n", - " 'pseudo_dir': pseudo_dir,\n", - " },\n", - " 'SYSTEM': {\n", - " 'occupations': 'smearing',\n", - " 'degauss': 0.02,\n", - " }\n", - "}" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-01-19T22:49:01.810150126Z", - "start_time": "2024-01-19T22:49:01.795706014Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [], - "source": [ - "@dataclass\n", - "class QERelaxMaker(Maker):\n", - " name: str = \"QE Relax Maker\"\n", - "\n", - " pseudopotentials: dict = field(default_factory=lambda: pseudopotentials)\n", - " parameters: dict = field(default_factory=lambda: parameters_relax)\n", - "\n", - " # can have an output schema\n", - " @job\n", - " def make(self, structure: Structure):\n", - " # typically we start by copying outputs from previous uns\n", - "\n", - " # write inputs\n", - " write(\n", - " 'relax.pwi',\n", - " AseAtomsAdaptor.get_atoms(structure),\n", - " kpts=(3, 3, 3),\n", - " input_data=self.parameters,\n", - " pseudopotentials=self.pseudopotentials,\n", - " )\n", - " subprocess.check_output(\"OMP_NUM_THREADS=1 mpirun -np 1 pw.x -in relax.pwi > relax.pwo\", shell=True,\n", - " universal_newlines=True)\n", - " relaxed_structure = parse_pw('pwscf.xml')['ase_structure']\n", - "\n", - " return {\"structure\": AseAtomsAdaptor.get_structure(relaxed_structure)}" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-01-19T22:49:02.669548165Z", - "start_time": "2024-01-19T22:49:02.599035228Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [], - "source": [ - "@dataclass\n", - "class QEStaticMaker(Maker):\n", - " name: str = \"QE Static Maker\"\n", - "\n", - " # this is not really needed for usage but makes it more convenient\n", - " pseudopotentials: dict = field(default_factory=lambda: pseudopotentials)\n", - " parameters: dict = field(default_factory=lambda: parameters_static)\n", - "\n", - " # can have an output schema\n", - " @job\n", - " def make(self, structure: Structure):\n", - " # typically we start by copying outputs from previous uns\n", - "\n", - " # write inputs\n", - " write(\n", - " 'strain.pwi',\n", - " AseAtomsAdaptor.get_atoms(structure),\n", - " kpts=(3, 3, 3),\n", - " input_data=self.parameters,\n", - " pseudopotentials=self.pseudopotentials,\n", - " )\n", - " subprocess.check_output(\"OMP_NUM_THREADS=1 mpirun -np 1 pw.x -in strain.pwi > strain.pwo\", shell=True,\n", - " universal_newlines=True)\n", - " results = parse_pw('pwscf.xml')\n", - " return {\"energy\": results['energy'], \"volume\": results['ase_structure'].get_volume()}\n" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-01-19T22:49:04.176084944Z", - "start_time": "2024-01-19T22:49:04.136222540Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "@job\n", - "def get_ev_curve(relaxed_structure, strain_min=0.9, strain_max=1.1, num_volumes=5):\n", - " # structure to atoms\n", - " relaxed_structure = AseAtomsAdaptor().get_atoms(relaxed_structure)\n", - " jobs = []\n", - " volumes = []\n", - " energies = []\n", - " for strain in np.linspace(strain_min, strain_max, num_volumes):\n", - " structure_strain = relaxed_structure.copy()\n", - " structure_strain.set_cell(structure_strain.cell * strain ** (1 / 3), scale_atoms=True)\n", - "\n", - " new_job = QEStaticMaker().make(AseAtomsAdaptor.get_structure(structure_strain))\n", - " jobs.append(new_job)\n", - " volumes.append(new_job.output[\"volume\"])\n", - " energies.append(new_job.output[\"energy\"])\n", - " return Response(replace=Flow(jobs, output={\"energies\": energies, \"volumes\": volumes}))\n" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-01-19T22:49:05.697083020Z", - "start_time": "2024-01-19T22:49:05.682147382Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [], - "source": [ - "@job\n", - "def get_plot(volume_lst, energy_lst):\n", - " plt.plot(volume_lst, energy_lst)\n", - " plt.xlabel(\"Volume\")\n", - " plt.ylabel(\"Energy\")" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-01-19T22:49:06.934391273Z", - "start_time": "2024-01-19T22:49:06.891332032Z" - } - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-19 23:49:08,251 INFO Started executing jobs locally\n", - "2024-01-19 23:49:08,562 INFO Starting job - QE Relax Maker (4ea734b6-01fc-4c9b-8864-7d670a5527da)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-19 23:50:43,819 INFO Finished job - QE Relax Maker (4ea734b6-01fc-4c9b-8864-7d670a5527da)\n", - "2024-01-19 23:50:43,827 INFO Starting job - get_ev_curve (5452ddbd-fba4-4e16-80ab-2812137b5f0e)\n", - "2024-01-19 23:50:43,888 INFO Finished job - get_ev_curve (5452ddbd-fba4-4e16-80ab-2812137b5f0e)\n", - "2024-01-19 23:50:43,960 INFO Starting job - QE Static Maker (8f5a7f51-c32b-4478-979c-78f345f1863a)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-19 23:50:57,671 INFO Finished job - QE Static Maker (8f5a7f51-c32b-4478-979c-78f345f1863a)\n", - "2024-01-19 23:50:57,678 INFO Starting job - QE Static Maker (e47037c0-e8eb-4b00-8f9e-753ba001b931)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-19 23:51:09,531 INFO Finished job - QE Static Maker (e47037c0-e8eb-4b00-8f9e-753ba001b931)\n", - "2024-01-19 23:51:09,543 INFO Starting job - QE Static Maker (888b3102-2474-4dbf-adbc-a28b6ceda79b)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-19 23:51:22,394 INFO Finished job - QE Static Maker (888b3102-2474-4dbf-adbc-a28b6ceda79b)\n", - "2024-01-19 23:51:22,406 INFO Starting job - QE Static Maker (7081ec13-a5bd-425c-9e71-4091fb898790)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-19 23:51:37,658 INFO Finished job - QE Static Maker (7081ec13-a5bd-425c-9e71-4091fb898790)\n", - "2024-01-19 23:51:37,678 INFO Starting job - QE Static Maker (77b0864a-e1d9-4ae4-b6fa-a62a700a252f)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-01-19 23:51:51,346 INFO Finished job - QE Static Maker (77b0864a-e1d9-4ae4-b6fa-a62a700a252f)\n", - "2024-01-19 23:51:51,370 INFO Starting job - store_inputs (5452ddbd-fba4-4e16-80ab-2812137b5f0e, 2)\n", - "2024-01-19 23:51:51,378 INFO Finished job - store_inputs (5452ddbd-fba4-4e16-80ab-2812137b5f0e, 2)\n", - "2024-01-19 23:51:51,382 INFO Starting job - get_plot (a949342a-e314-4e9a-ae5c-988cdd6fe25b)\n", - "2024-01-19 23:51:51,539 INFO Finished job - get_plot (a949342a-e314-4e9a-ae5c-988cdd6fe25b)\n", - "2024-01-19 23:51:51,544 INFO Finished executing jobs locally\n" - ] - }, - { - "data": { - "text/plain": "{'4ea734b6-01fc-4c9b-8864-7d670a5527da': {1: Response(output={'structure': Structure Summary\n Lattice\n abc : 4.045218941864792 4.045218941864792 4.045218941864792\n angles : 90.0 90.0 90.0\n volume : 66.19513870350409\n A : 4.045218941864792 0.0 0.0\n B : 0.0 4.045218941864792 0.0\n C : 0.0 0.0 4.045218941864792\n pbc : True True True\n PeriodicSite: Al (0.0, 0.0, 0.0) [0.0, 0.0, 0.0]\n PeriodicSite: Al (0.0, 2.023, 2.023) [0.0, 0.5, 0.5]\n PeriodicSite: Al (2.023, 0.0, 2.023) [0.5, 0.0, 0.5]\n PeriodicSite: Al (2.023, 2.023, 0.0) [0.5, 0.5, 0.0]}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '5452ddbd-fba4-4e16-80ab-2812137b5f0e': {1: Response(output=None, detour=None, addition=None, replace=Flow(name='Flow', uuid='7492a13f-9879-40a0-ad3a-bbc7a599e20b')\n 1. Job(name='QE Static Maker', uuid='8f5a7f51-c32b-4478-979c-78f345f1863a')\n 2. Job(name='QE Static Maker', uuid='e47037c0-e8eb-4b00-8f9e-753ba001b931')\n 3. Job(name='QE Static Maker', uuid='888b3102-2474-4dbf-adbc-a28b6ceda79b')\n 4. Job(name='QE Static Maker', uuid='7081ec13-a5bd-425c-9e71-4091fb898790')\n 5. Job(name='QE Static Maker', uuid='77b0864a-e1d9-4ae4-b6fa-a62a700a252f')\n 6. Job(name='store_inputs', uuid='5452ddbd-fba4-4e16-80ab-2812137b5f0e'), stored_data=None, stop_children=False, stop_jobflow=False),\n 2: Response(output={'energies': [OutputReference(8f5a7f51-c32b-4478-979c-78f345f1863a, ['energy']), OutputReference(e47037c0-e8eb-4b00-8f9e-753ba001b931, ['energy']), OutputReference(888b3102-2474-4dbf-adbc-a28b6ceda79b, ['energy']), OutputReference(7081ec13-a5bd-425c-9e71-4091fb898790, ['energy']), OutputReference(77b0864a-e1d9-4ae4-b6fa-a62a700a252f, ['energy'])], 'volumes': [OutputReference(8f5a7f51-c32b-4478-979c-78f345f1863a, ['volume']), OutputReference(e47037c0-e8eb-4b00-8f9e-753ba001b931, ['volume']), OutputReference(888b3102-2474-4dbf-adbc-a28b6ceda79b, ['volume']), OutputReference(7081ec13-a5bd-425c-9e71-4091fb898790, ['volume']), OutputReference(77b0864a-e1d9-4ae4-b6fa-a62a700a252f, ['volume'])]}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '8f5a7f51-c32b-4478-979c-78f345f1863a': {1: Response(output={'energy': -1074.8451830768136, 'volume': 59.57562405194964}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n 'e47037c0-e8eb-4b00-8f9e-753ba001b931': {1: Response(output={'energy': -1074.915894738579, 'volume': 62.88538094372461}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '888b3102-2474-4dbf-adbc-a28b6ceda79b': {1: Response(output={'energy': -1074.936525208784, 'volume': 66.19513783549975}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '7081ec13-a5bd-425c-9e71-4091fb898790': {1: Response(output={'energy': -1074.9194989201578, 'volume': 69.5048947272748}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '77b0864a-e1d9-4ae4-b6fa-a62a700a252f': {1: Response(output={'energy': -1074.874179782166, 'volume': 72.81465161904978}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n 'a949342a-e314-4e9a-ae5c-988cdd6fe25b': {1: Response(output=None, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)}}" - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "structure = bulk('Al', a=4.15, cubic=True)\n", - "relax = QERelaxMaker().make(AseAtomsAdaptor.get_structure(structure))\n", - "ev_curve = get_ev_curve(relax.output[\"structure\"])\n", - "plot = get_plot(volume_lst=ev_curve.output[\"volumes\"], energy_lst=ev_curve.output[\"energies\"])\n", - "jobs = [relax, ev_curve, plot]\n", - "run_locally(Flow(jobs), create_folders=True)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-01-19T22:51:52.014356368Z", - "start_time": "2024-01-19T22:49:08.190213201Z" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/eos/atomate2/ev_curve_universal_forcefields.ipynb b/eos/atomate2/ev_curve_universal_forcefields.ipynb deleted file mode 100644 index bc0869f..0000000 --- a/eos/atomate2/ev_curve_universal_forcefields.ipynb +++ /dev/null @@ -1,369 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-10-31T16:44:06.639711009Z", - "start_time": "2023-10-31T16:44:02.029650200Z" - } - }, - "outputs": [], - "source": [ - "%%capture\n", - "!pip install atomate2[strict]" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-10-31T16:44:06.647530675Z", - "start_time": "2023-10-31T16:44:06.645508007Z" - } - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "is_executing": true, - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "First we load makers from atomate2. Makers can be used to generate Flow objects with default values in a convenient way." - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-10-31T16:44:06.692176831Z", - "start_time": "2023-10-31T16:44:06.651463438Z" - } - }, - "outputs": [], - "source": [ - "#from atomate2.vasp.jobs.core import RelaxMaker, StaticMaker, RelaxSetGenerator\n", - "from atomate2.forcefields.jobs import CHGNetRelaxMaker, CHGNetStaticMaker, M3GNetRelaxMaker, M3GNetStaticMaker, GAPRelaxMaker, GAPStaticMaker\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, we need standard classes, decorators, functions from jobflow" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-31T16:44:06.694884313Z", - "start_time": "2023-10-31T16:44:06.692543331Z" - } - }, - "outputs": [], - "source": [ - "from jobflow import job, Response, Flow, run_locally, Maker\n", - "from dataclasses import dataclass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We define a job to create a flow including constant volume optimizations" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-10-31T16:44:06.695215746Z", - "start_time": "2023-10-31T16:44:06.692916540Z" - } - }, - "outputs": [], - "source": [ - "@job\n", - "def get_energies(structure, volume_increments=[0.95,0.97,0.99, 1.00, 1.01, 1.03, 1.05 ]):\n", - " jobs=[]\n", - " volumes=[]\n", - " energies=[]\n", - " for inc in volume_increments:\n", - " new_structure=structure.copy()\n", - " new_structure.scale_lattice(structure.volume*inc)\n", - " # job=RelaxMaker(input_set_generator=RelaxSetGenerator(user_incar_settings={\"ISIF\": 4})).make(new_structure)\n", - " new_job=M3GNetRelaxMaker(relax_cell=False, relax_kwargs={\"fmax\": 0.0001}).make(new_structure)\n", - " jobs.append(new_job)\n", - " volumes.append(new_job.output.structure.volume)\n", - " energies.append(new_job.output.output.energy)\n", - " return Response(replace=Flow(jobs, output={\"energies\": energies, \"volumes\": volumes}))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We then compute the ev curve in a job starting from a list of energies, volumes" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-10-31T16:44:06.695481955Z", - "start_time": "2023-10-31T16:44:06.693177083Z" - } - }, - "outputs": [], - "source": [ - "from ase.units import kJ\n", - "from ase.eos import EquationOfState\n", - "\n", - "@job\n", - "def get_results_ev_curve(list_volumes, list_energies):\n", - " eos = EquationOfState(list_volumes, list_energies)\n", - " v0, e0, B = eos.fit()\n", - " eos.plot('eos.png')\n", - "\n", - " results = {}\n", - " results[\"V0\"] = v0\n", - " results[\"e0\"] = e0\n", - " results[\"B\"] = B\n", - "\n", - " return results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We define a Maker for convenience" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-10-31T16:44:06.735983869Z", - "start_time": "2023-10-31T16:44:06.693409550Z" - } - }, - "outputs": [], - "source": [ - "@dataclass\n", - "class EVMaker(Maker):\n", - " name=\"ev\"\n", - " def make(self, structure):\n", - "\n", - " job1=M3GNetRelaxMaker(relax_cell=True, relax_kwargs={\"fmax\": 0.0001}).make(structure=structure)\n", - " job2=get_energies(job1.output.structure)\n", - " job3=get_results_ev_curve(list_volumes=job2.output[\"volumes\"], list_energies=job2.output[\"energies\"])\n", - " return Flow([job1,job2,job3], job3.output)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Start the workflow" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-10-31T16:44:06.738637994Z", - "start_time": "2023-10-31T16:44:06.736329235Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "40.692834\n" - ] - } - ], - "source": [ - "from pymatgen.core import Structure\n", - "structure = Structure(\n", - " lattice=[[0, 2.73, 2.73], [2.73, 0, 2.73], [2.73, 2.73, 0]],\n", - " species=[\"Si\", \"Si\"],\n", - " coords=[[0, 0, 0], [0.25, 0.25, 0.25]],\n", - ")\n", - "print(structure.volume)\n", - "flow=EVMaker().make(structure=structure)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, we run it." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-10-31T16:44:14.635918080Z", - "start_time": "2023-10-31T16:44:06.736690201Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2023-10-31 17:44:06,735 INFO Started executing jobs locally\n", - "2023-10-31 17:44:06,739 INFO Starting job - M3GNet relax (af81b0e7-7f82-4437-b290-638de98124c8)\n", - "2023-10-31 17:44:10,831 INFO Finished job - M3GNet relax (af81b0e7-7f82-4437-b290-638de98124c8)\n", - "2023-10-31 17:44:10,837 INFO Starting job - get_energies (aa500484-b2f7-4d30-8823-be55abe104f0)\n", - "2023-10-31 17:44:11,039 INFO Finished job - get_energies (aa500484-b2f7-4d30-8823-be55abe104f0)\n", - "2023-10-31 17:44:11,048 INFO Starting job - M3GNet relax (1e4dbfd0-a9c5-4563-a519-4f30bff6fd38)\n", - "2023-10-31 17:44:11,395 INFO Finished job - M3GNet relax (1e4dbfd0-a9c5-4563-a519-4f30bff6fd38)\n", - "2023-10-31 17:44:11,397 INFO Starting job - M3GNet relax (cac4d8aa-1bd6-4604-8a08-cab4d96d2e7d)\n", - "2023-10-31 17:44:11,734 INFO Finished job - M3GNet relax (cac4d8aa-1bd6-4604-8a08-cab4d96d2e7d)\n", - "2023-10-31 17:44:11,738 INFO Starting job - M3GNet relax (4744ce86-a252-4b29-9f16-534c105eb05c)\n", - "2023-10-31 17:44:12,080 INFO Finished job - M3GNet relax (4744ce86-a252-4b29-9f16-534c105eb05c)\n", - "2023-10-31 17:44:12,081 INFO Starting job - M3GNet relax (1d5b588c-c6e8-4d09-9417-67beea9fcec7)\n", - "2023-10-31 17:44:12,420 INFO Finished job - M3GNet relax (1d5b588c-c6e8-4d09-9417-67beea9fcec7)\n", - "2023-10-31 17:44:12,422 INFO Starting job - M3GNet relax (56132601-b9f6-42de-ba8d-d60689fc152e)\n", - "2023-10-31 17:44:12,770 INFO Finished job - M3GNet relax (56132601-b9f6-42de-ba8d-d60689fc152e)\n", - "2023-10-31 17:44:12,778 INFO Starting job - M3GNet relax (9fb5d3a3-2fd9-4211-80a8-4fe7a9acfbd1)\n", - "2023-10-31 17:44:13,114 INFO Finished job - M3GNet relax (9fb5d3a3-2fd9-4211-80a8-4fe7a9acfbd1)\n", - "2023-10-31 17:44:13,116 INFO Starting job - M3GNet relax (33b87584-ec0e-44fe-92b4-a6ff869fe3e5)\n", - "2023-10-31 17:44:14,222 INFO Finished job - M3GNet relax (33b87584-ec0e-44fe-92b4-a6ff869fe3e5)\n", - "2023-10-31 17:44:14,228 INFO Starting job - store_inputs (aa500484-b2f7-4d30-8823-be55abe104f0, 2)\n", - "2023-10-31 17:44:14,232 INFO Finished job - store_inputs (aa500484-b2f7-4d30-8823-be55abe104f0, 2)\n", - "2023-10-31 17:44:14,232 INFO Starting job - get_results_ev_curve (0762de80-6ad6-4ab7-84d0-e3d5717646f8)\n", - "2023-10-31 17:44:14,433 INFO Finished job - get_results_ev_curve (0762de80-6ad6-4ab7-84d0-e3d5717646f8)\n", - "2023-10-31 17:44:14,434 INFO Finished executing jobs locally\n" - ] - }, - { - "data": { - "text/plain": "
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "response=run_locally(flow)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-10-31T16:44:14.636842650Z", - "start_time": "2023-10-31T16:44:14.634697849Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'V0': 40.65357432923033, 'e0': -10.711596589376484, 'B': 0.4652098738365982}\n" - ] - } - ], - "source": [ - "print(response[flow[2].uuid][1].output)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "ExecuteTime": { - "end_time": "2023-10-31T16:44:14.754434579Z", - "start_time": "2023-10-31T16:44:14.636412010Z" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/eos/atomate2/pseudo/Al.pbe-n-kjpaw_psl.1.0.0.UPF b/eos/atomate2/pseudo/Al.pbe-n-kjpaw_psl.1.0.0.UPF deleted file mode 100644 index 348d039..0000000 --- a/eos/atomate2/pseudo/Al.pbe-n-kjpaw_psl.1.0.0.UPF +++ /dev/null @@ -1,18497 +0,0 @@ - - - Generated using "atomic" code by A. Dal Corso v.5.1 - Author: ADC - Generation date: 10Oct2014 - Pseudopotential type: PAW - Element: Al - Functional: SLA PW PBX PBC - - Suggested minimum cutoff for wavefunctions: 29. Ry - Suggested minimum cutoff for charge density: 143. Ry - The Pseudo was generated with a Scalar-Relativistic Calculation - Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 1.9000 - Pseudopotential contains additional information for GIPAW reconstruction. - - Valence configuration: - nl pn l occ Rcut Rcut US E pseu - 3S 1 0 2.00 1.700 1.900 -0.569826 - 3P 2 1 1.00 1.700 2.000 -0.199338 - Generation configuration: - 3S 1 0 2.00 1.700 1.900 -0.569824 - 3S 1 0 0.00 1.700 1.900 6.000000 - 3P 2 1 1.00 1.700 2.000 -0.199338 - 3P 2 1 0.00 1.700 2.000 3.500000 - 3D 3 2 0.00 1.700 1.900 0.100000 - 3D 3 2 0.00 1.700 1.900 0.300000 - - Pseudization used: troullier-martins - - &input - title='Al', - zed=13., - rel=1, - config='[Ne] 3s2 3p1 3d-1', - iswitch=3, - dft='PBE' - / - &inputp - lpaw=.true., - pseudotype=3, - file_pseudopw='Al.pbe-n-kjpaw_psl.1.0.0.UPF', - author='ADC', - lloc=-1, - rcloc=1.9, - which_augfun='PSQ', - rmatch_augfun_nc=.true., - nlcc=.true., - new_core_ps=.true., - rcore=1.8, - tm=.true. - / -6 -3S 1 0 2.00 0.00 1.70 1.90 0.0 -3S 1 0 0.00 6.00 1.70 1.90 0.0 -3P 2 1 1.00 0.00 1.70 2.00 0.0 -3P 2 1 0.00 3.50 1.70 2.00 0.0 -3D 3 2 0.00 0.10 1.70 1.90 0.0 -3D 3 2 0.00 0.30 1.70 1.90 0.0 - - - - - - - - - 7.014476658111663E-005 7.102707912838632E-005 7.192048980127555E-005 7.282513819701954E-005 - 7.374116566877076E-005 7.466871534768530E-005 7.560793216528758E-005 7.655896287611625E-005 - 7.752195608065465E-005 7.849706224855035E-005 7.948443374212593E-005 8.048422484018604E-005 - 8.149659176212386E-005 8.252169269233053E-005 8.355968780491215E-005 8.461073928871678E-005 - 8.567501137267717E-005 8.675267035147172E-005 8.784388461150826E-005 8.894882465723522E-005 - 9.006766313778265E-005 9.120057487393945E-005 9.234773688546930E-005 9.350932841877036E-005 - 9.468553097488315E-005 9.587652833784997E-005 9.708250660343188E-005 9.830365420818649E-005 - 9.954016195891138E-005 1.007922230624585E-004 1.020600331559224E-004 1.033437903372098E-004 - 1.046436951959918E-004 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-1,18497 +0,0 @@ - - - Generated using "atomic" code by A. Dal Corso v.5.1 - Author: ADC - Generation date: 10Oct2014 - Pseudopotential type: PAW - Element: Al - Functional: SLA PW PBX PBC - - Suggested minimum cutoff for wavefunctions: 29. Ry - Suggested minimum cutoff for charge density: 143. Ry - The Pseudo was generated with a Scalar-Relativistic Calculation - Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 1.9000 - Pseudopotential contains additional information for GIPAW reconstruction. - - Valence configuration: - nl pn l occ Rcut Rcut US E pseu - 3S 1 0 2.00 1.700 1.900 -0.569826 - 3P 2 1 1.00 1.700 2.000 -0.199338 - Generation configuration: - 3S 1 0 2.00 1.700 1.900 -0.569824 - 3S 1 0 0.00 1.700 1.900 6.000000 - 3P 2 1 1.00 1.700 2.000 -0.199338 - 3P 2 1 0.00 1.700 2.000 3.500000 - 3D 3 2 0.00 1.700 1.900 0.100000 - 3D 3 2 0.00 1.700 1.900 0.300000 - - Pseudization used: troullier-martins - - &input - title='Al', - zed=13., - rel=1, - config='[Ne] 3s2 3p1 3d-1', - iswitch=3, - dft='PBE' - / - &inputp - lpaw=.true., - pseudotype=3, - file_pseudopw='Al.pbe-n-kjpaw_psl.1.0.0.UPF', - author='ADC', - lloc=-1, - rcloc=1.9, - which_augfun='PSQ', - rmatch_augfun_nc=.true., - nlcc=.true., - new_core_ps=.true., - rcore=1.8, - tm=.true. - / -6 -3S 1 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0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 - 0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 - 0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 - 0.000000000000000E+000 0.000000000000000E+000 0.000000000000000E+000 - - - - diff --git a/eos/basic/quantumespresso-ev.ipynb b/eos/basic/quantumespresso-ev.ipynb deleted file mode 100644 index f286ce7..0000000 --- a/eos/basic/quantumespresso-ev.ipynb +++ /dev/null @@ -1,228 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import subprocess\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from ase.build import bulk\n", - "from ase.io import write\n", - "\n", - "from adis_tools.parsers import parse_pw" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Quantum Espresso Inputs" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "pseudopotentials = {\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"}" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "parameters = {\n", - " 'CONTROL': {\n", - " 'calculation': 'vc-relax',\n", - " 'pseudo_dir': 'pseudo',\n", - " },\n", - " 'SYSTEM': {\n", - " 'occupations': 'smearing',\n", - " 'degauss': 0.02,\n", - " }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Workflow" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Structure Optimization" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "structure = bulk('Al', a=4.15, cubic=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "write(\n", - " 'relax.pwi',\n", - " structure, \n", - " kpts=(3, 3, 3), \n", - " input_data=parameters, \n", - " pseudopotentials=pseudopotentials,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "''" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "subprocess.check_output(\"OMP_NUM_THREADS=1 mpirun -np 1 pw.x -in relax.pwi > relax.pwo\", shell=True, universal_newlines=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "relaxed_structure = parse_pw('pwscf.xml')['ase_structure']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Energy Volume Curve" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "parameters['CONTROL']['calculation'] = 'scf'\n", - "\n", - "energy_lst, volume_lst = [], []\n", - "\n", - "for strain in np.linspace(0.9, 1.1, 5):\n", - "\n", - " structure_strain = relaxed_structure.copy()\n", - " structure_strain.set_cell(structure_strain.cell * strain**(1/3), scale_atoms=True)\n", - "\n", - " write(\n", - " 'strain.pwi', \n", - " structure_strain, \n", - " kpts=(3, 3, 3), \n", - " input_data=parameters, \n", - " pseudopotentials=pseudopotentials,\n", - " )\n", - " subprocess.check_output(\"OMP_NUM_THREADS=1 mpirun -np 1 pw.x -in strain.pwi > strain.pwo\", shell=True, universal_newlines=True)\n", - " results = parse_pw('pwscf.xml')\n", - " energy_lst.append(results['energy'])\n", - " volume_lst.append(results['ase_structure'].get_volume())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Result" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Energy')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(volume_lst, energy_lst)\n", - "plt.xlabel(\"Volume\")\n", - "plt.ylabel(\"Energy\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/eos/pyiron/lammps.ipynb b/eos/pyiron/lammps.ipynb deleted file mode 100644 index 51011f1..0000000 --- a/eos/pyiron/lammps.ipynb +++ /dev/null @@ -1,1438 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "0881f9fb-e651-4e36-9a11-9fb9aea01617", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8ac763fa2c964d7b92cc0d23f1c1bab9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pyiron import Project\n", - "import numpy as np\n", - "import matplotlib.pylab as plt\n", - "\n", - "%config IPCompleter.evaluation='unsafe'" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "9adb86d1-4fad-485d-b942-dc6aed85e77a", - "metadata": {}, - "outputs": [], - "source": [ - "job = pr.load('job_0d1')\n", - "job.decompress()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "c5ee7dc3-832e-4c92-935e-d1d5cb786cae", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ParameterValueComment
0unitsmetal
1dimension3
2boundaryp p p
3atom_styleatomic
4read_datastructure.inp
5includepotential.inp
6fix___ensembleall nve
7variable___dumptimeequal 100
8variable___thermotimeequal 100
9min_stylecg
10dump___1all custom ${dumptime} dump.out id type xsu ysu zsu fx fy fz vx vy vz
11dump_modify___1sort id format line \"%d %d %20.15g %20.15g %20.15g %20.15g %20.15g %20.15g %20.15g %20.15g %20.15g\"
12thermo_stylecustom step temp pe etotal pxx pxy pxz pyy pyz pzz vol
13thermo_modifyformat float %20.15g
14thermo${thermotime}
15minimize0.0 0.0001 100000 10000000
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" - ], - "text/plain": [ - " Parameter \\\n", - "0 units \n", - "1 dimension \n", - "2 boundary \n", - "3 atom_style \n", - "4 read_data \n", - "5 include \n", - "6 fix___ensemble \n", - "7 variable___dumptime \n", - "8 variable___thermotime \n", - "9 min_style \n", - "10 dump___1 \n", - "11 dump_modify___1 \n", - "12 thermo_style \n", - "13 thermo_modify \n", - "14 thermo \n", - "15 minimize \n", - "\n", - " Value \\\n", - "0 metal \n", - "1 3 \n", - "2 p p p \n", - "3 atomic \n", - "4 structure.inp \n", - "5 potential.inp \n", - "6 all nve \n", - "7 equal 100 \n", - "8 equal 100 \n", - "9 cg \n", - "10 all custom ${dumptime} dump.out id type xsu ysu zsu fx fy fz vx vy vz \n", - "11 sort id format line \"%d %d %20.15g %20.15g %20.15g %20.15g %20.15g %20.15g %20.15g %20.15g %20.15g\" \n", - "12 custom step temp pe etotal pxx pxy pxz pyy pyz pzz vol \n", - "13 format float %20.15g \n", - "14 ${thermotime} \n", - "15 0.0 0.0001 100000 10000000 \n", - "\n", - " Comment \n", - "0 \n", - "1 \n", - "2 \n", - "3 \n", - "4 \n", - "5 \n", - "6 \n", - "7 \n", - "8 \n", - "9 \n", - "10 \n", - "11 \n", - "12 \n", - "13 \n", - "14 \n", - "15 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "element = 'Al'\n", - "\n", - "pr = Project(f'eV_{element}')\n", - "\n", - "bulk = pr.create.structure.bulk(element, cubic=True)\n", - "\n", - "job_relax = pr.create.job.Lammps('relax')\n", - "job_relax.structure = bulk\n", - "job_relax.calc_minimize(ionic_force_tolerance=0.0001)\n", - "job_relax.calc_" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "94a3c22a-534a-498f-8853-70fad70bbb0f", - "metadata": {}, - "outputs": [], - "source": [ - "job = pr.load('relax')" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "4b7c3acc-a06e-47b0-8136-f22e86bab200", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "#!/bin/bash\n", - "exec lmp_serial -in control.inp;\n" - ] - } - ], - "source": [ - "!cat $job.executable" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "31d5d8ac-9333-4bd5-8825-b24ed9a60769", - "metadata": {}, - "outputs": [], - "source": [ - "job_relax.remove()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "28f3fed4-3a6c-49b1-9bdf-21c9fe74dfd0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'finished'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "job_relax.status" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "7f3ef3b0-94ed-4918-8134-114427b14be4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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idstatuschemicalformulajobsubjobprojectpathprojecttimestarttimestoptotalcputimecomputerhamiltonhamversionparentidmasterid
0317finishedAl4job_m0_1/job_m0_1None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-10-31 16:45:32.3312892023-10-31 16:45:32.4763870.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
1318finishedAl4job_m0_08/job_m0_08None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-10-31 16:45:32.6221292023-10-31 16:45:32.7648970.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
2319finishedAl4job_m0_06/job_m0_06None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-10-31 16:45:32.9077292023-10-31 16:45:33.0526740.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
3320finishedAl4job_m0_04/job_m0_04None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-10-31 16:45:33.1998552023-10-31 16:45:33.3457540.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
4321finishedAl4job_m0_02/job_m0_02None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-10-31 16:45:33.4883422023-10-31 16:45:33.6304720.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
5322finishedAl4job_0_0/job_0_0None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-10-31 16:45:33.7710592023-10-31 16:45:33.9148250.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
6323finishedAl4job_0_02/job_0_02None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-10-31 16:45:34.0560512023-10-31 16:45:34.1974720.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
7324finishedAl4job_0_04/job_0_04None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-10-31 16:45:34.3384642023-10-31 16:45:34.4818630.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
8325finishedAl4job_0_06/job_0_06None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-10-31 16:45:34.6268342023-10-31 16:45:34.7729010.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
9326finishedAl4job_0_08/job_0_08None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-10-31 16:45:34.9157182023-10-31 16:45:35.0612490.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
10327finishedAl4job_0_1/job_0_1None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-10-31 16:45:35.2040032023-10-31 16:45:35.3515430.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
11328finishedNoneeV_table/eV_tableNone/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-10-31 16:48:55.2020302023-10-31 16:48:55.2620910.0pyiron@cmadmins-MacBook-Air.local#1TableJob0.1NoneNone
12329finishedAl4relax/relaxNone/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-11-02 16:20:43.7396882023-11-02 16:20:44.5790590.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
13330finishedAl4job_m0d1/job_m0d1None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-11-02 16:37:52.7930902023-11-02 16:37:52.9752160.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
14331finishedAl4job_m0d08/job_m0d08None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-11-02 16:37:53.1229582023-11-02 16:37:53.2743300.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
15332finishedAl4job_m0d06/job_m0d06None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-11-02 16:37:53.4349862023-11-02 16:37:53.5930240.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
16333finishedAl4job_m0d04/job_m0d04None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-11-02 16:37:53.7478522023-11-02 16:37:53.8992080.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
17334finishedAl4job_m0d02/job_m0d02None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-11-02 16:37:54.0520822023-11-02 16:37:54.1998170.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
18335finishedAl4job_0d0/job_0d0None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-11-02 16:37:54.3539762023-11-02 16:37:54.5081490.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
19336finishedAl4job_0d02/job_0d02None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-11-02 16:37:54.6676102023-11-02 16:37:54.8334200.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
20337finishedAl4job_0d04/job_0d04None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-11-02 16:37:54.9999372023-11-02 16:37:55.1639990.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
21338finishedAl4job_0d06/job_0d06None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-11-02 16:37:55.3137742023-11-02 16:37:55.4704210.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
22339finishedAl4job_0d08/job_0d08None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-11-02 16:37:55.6216102023-11-02 16:37:55.7866730.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
23340finishedAl4job_0d1/job_0d1None/Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/2023-11-02 16:37:55.9524052023-11-02 16:37:56.1053030.0pyiron@cmadmins-MacBook-Air.local#1Lammps0.1NoneNone
\n", - "
" - ], - "text/plain": [ - " id status chemicalformula job subjob projectpath \\\n", - "0 317 finished Al4 job_m0_1 /job_m0_1 None \n", - "1 318 finished Al4 job_m0_08 /job_m0_08 None \n", - "2 319 finished Al4 job_m0_06 /job_m0_06 None \n", - "3 320 finished Al4 job_m0_04 /job_m0_04 None \n", - "4 321 finished Al4 job_m0_02 /job_m0_02 None \n", - "5 322 finished Al4 job_0_0 /job_0_0 None \n", - "6 323 finished Al4 job_0_02 /job_0_02 None \n", - "7 324 finished Al4 job_0_04 /job_0_04 None \n", - "8 325 finished Al4 job_0_06 /job_0_06 None \n", - "9 326 finished Al4 job_0_08 /job_0_08 None \n", - "10 327 finished Al4 job_0_1 /job_0_1 None \n", - "11 328 finished None eV_table /eV_table None \n", - "12 329 finished Al4 relax /relax None \n", - "13 330 finished Al4 job_m0d1 /job_m0d1 None \n", - "14 331 finished Al4 job_m0d08 /job_m0d08 None \n", - "15 332 finished Al4 job_m0d06 /job_m0d06 None \n", - "16 333 finished Al4 job_m0d04 /job_m0d04 None \n", - "17 334 finished Al4 job_m0d02 /job_m0d02 None \n", - "18 335 finished Al4 job_0d0 /job_0d0 None \n", - "19 336 finished Al4 job_0d02 /job_0d02 None \n", - "20 337 finished Al4 job_0d04 /job_0d04 None \n", - "21 338 finished Al4 job_0d06 /job_0d06 None \n", - "22 339 finished Al4 job_0d08 /job_0d08 None \n", - "23 340 finished Al4 job_0d1 /job_0d1 None \n", - "\n", - " project \\\n", - "0 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "1 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "2 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "3 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "4 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "5 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "6 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "7 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "8 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "9 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "10 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "11 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "12 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "13 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "14 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "15 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "16 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "17 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "18 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "19 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "20 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "21 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "22 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "23 /Users/joerg/python_projects/git_libs/pyiron_workflow/notebooks/eV_Al/ \n", - "\n", - " timestart timestop totalcputime \\\n", - "0 2023-10-31 16:45:32.331289 2023-10-31 16:45:32.476387 0.0 \n", - "1 2023-10-31 16:45:32.622129 2023-10-31 16:45:32.764897 0.0 \n", - "2 2023-10-31 16:45:32.907729 2023-10-31 16:45:33.052674 0.0 \n", - "3 2023-10-31 16:45:33.199855 2023-10-31 16:45:33.345754 0.0 \n", - "4 2023-10-31 16:45:33.488342 2023-10-31 16:45:33.630472 0.0 \n", - "5 2023-10-31 16:45:33.771059 2023-10-31 16:45:33.914825 0.0 \n", - "6 2023-10-31 16:45:34.056051 2023-10-31 16:45:34.197472 0.0 \n", - "7 2023-10-31 16:45:34.338464 2023-10-31 16:45:34.481863 0.0 \n", - "8 2023-10-31 16:45:34.626834 2023-10-31 16:45:34.772901 0.0 \n", - "9 2023-10-31 16:45:34.915718 2023-10-31 16:45:35.061249 0.0 \n", - "10 2023-10-31 16:45:35.204003 2023-10-31 16:45:35.351543 0.0 \n", - "11 2023-10-31 16:48:55.202030 2023-10-31 16:48:55.262091 0.0 \n", - "12 2023-11-02 16:20:43.739688 2023-11-02 16:20:44.579059 0.0 \n", - "13 2023-11-02 16:37:52.793090 2023-11-02 16:37:52.975216 0.0 \n", - "14 2023-11-02 16:37:53.122958 2023-11-02 16:37:53.274330 0.0 \n", - "15 2023-11-02 16:37:53.434986 2023-11-02 16:37:53.593024 0.0 \n", - "16 2023-11-02 16:37:53.747852 2023-11-02 16:37:53.899208 0.0 \n", - "17 2023-11-02 16:37:54.052082 2023-11-02 16:37:54.199817 0.0 \n", - "18 2023-11-02 16:37:54.353976 2023-11-02 16:37:54.508149 0.0 \n", - "19 2023-11-02 16:37:54.667610 2023-11-02 16:37:54.833420 0.0 \n", - "20 2023-11-02 16:37:54.999937 2023-11-02 16:37:55.163999 0.0 \n", - "21 2023-11-02 16:37:55.313774 2023-11-02 16:37:55.470421 0.0 \n", - "22 2023-11-02 16:37:55.621610 2023-11-02 16:37:55.786673 0.0 \n", - "23 2023-11-02 16:37:55.952405 2023-11-02 16:37:56.105303 0.0 \n", - "\n", - " computer hamilton hamversion parentid masterid \n", - "0 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "1 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "2 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "3 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "4 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "5 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "6 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "7 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "8 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "9 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "10 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "11 pyiron@cmadmins-MacBook-Air.local#1 TableJob 0.1 None None \n", - "12 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "13 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "14 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "15 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "16 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "17 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "18 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "19 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "20 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "21 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "22 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None \n", - "23 pyiron@cmadmins-MacBook-Air.local#1 Lammps 0.1 None None " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pr.job_table()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "52b885b2-17a6-4dee-ba19-560bd56e1a5f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-11-02 16:37:52,575 - pyiron_log - WARNING - The job relax is being loaded instead of running. To re-run use the argument 'delete_existing_job=True in create_job'\n", - "/Users/joerg/mambaforge/envs/py11/lib/python3.11/site-packages/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The job job_m0d1 was saved and received the ID: 330\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/joerg/mambaforge/envs/py11/lib/python3.11/site-packages/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The job job_m0d08 was saved and received the ID: 331\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/joerg/mambaforge/envs/py11/lib/python3.11/site-packages/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The job job_m0d06 was saved and received the ID: 332\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/joerg/mambaforge/envs/py11/lib/python3.11/site-packages/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The job job_m0d04 was saved and received the ID: 333\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/joerg/mambaforge/envs/py11/lib/python3.11/site-packages/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The job job_m0d02 was saved and received the ID: 334\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/joerg/mambaforge/envs/py11/lib/python3.11/site-packages/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The job job_0d0 was saved and received the ID: 335\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/joerg/mambaforge/envs/py11/lib/python3.11/site-packages/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The job job_0d02 was saved and received the ID: 336\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/joerg/mambaforge/envs/py11/lib/python3.11/site-packages/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The job job_0d04 was saved and received the ID: 337\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/joerg/mambaforge/envs/py11/lib/python3.11/site-packages/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The job job_0d06 was saved and received the ID: 338\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/joerg/mambaforge/envs/py11/lib/python3.11/site-packages/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The job job_0d08 was saved and received the ID: 339\n", - "The job job_0d1 was saved and received the ID: 340\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/joerg/mambaforge/envs/py11/lib/python3.11/site-packages/pyiron_atomistics/lammps/base.py:294: UserWarning: No potential set via job.potential - use default potential, 1995--Angelo-J-E--Ni-Al-H--LAMMPS--ipr1\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "element = 'Al'\n", - "\n", - "pr = Project(f'eV_{element}')\n", - "\n", - "bulk = pr.create.structure.bulk(element, cubic=True)\n", - "\n", - "job_relax = pr.create.job.Lammps('relax')\n", - "job_relax.structure = bulk\n", - "job_relax.calc_minimize(ionic_force_tolerance=0.0001)\n", - "\n", - "job_relax.run()\n", - "\n", - "for strain in np.linspace(-0.1, 0.1, 11):\n", - " \n", - " job = pr.create.job.Lammps(('job', strain))\n", - " relaxed_structure = job_relax.get_structure(-1)\n", - " job.structure = relaxed_structure.apply_strain(strain,return_box=True)\n", - " job.run()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "d1663461-9cac-43e3-91a7-c8a9dc4b64cd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The job eV_table was saved and received the ID: 341\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "916ec138cbea4f65b73bf873721fbbc9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Loading and filtering jobs: 0%| | 0/24 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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job_idjob_nameenergy_totvolume
0317job_m0_1-11.42453148.427561
12330job_m0d1-11.42453148.427561
1318job_m0_08-12.22826551.728341
13331job_m0d08-12.22826551.728341
2319job_m0_06-12.79942455.175799
14332job_m0d06-12.79942455.175799
3320job_m0_04-13.17231758.773123
15333job_m0d04-13.17231758.773123
4321job_m0_02-13.37705162.523502
16334job_m0d02-13.37705162.523502
17335job_0d0-13.44000066.430125
11329relax-13.44000066.430125
5322job_0_0-13.44000066.430125
6323job_0_02-13.38422670.496180
18336job_0d02-13.38422670.496180
7324job_0_04-13.22985974.724856
19337job_0d04-13.22985974.724856
8325job_0_06-12.99442779.119342
20338job_0d06-12.99442779.119342
9326job_0_08-12.69316083.682826
21339job_0d08-12.69316083.682826
10327job_0_1-12.33925488.418496
22340job_0d1-12.33925488.418496
\n", - "" - ], - "text/plain": [ - " job_id job_name energy_tot volume\n", - "0 317 job_m0_1 -11.424531 48.427561\n", - "12 330 job_m0d1 -11.424531 48.427561\n", - "1 318 job_m0_08 -12.228265 51.728341\n", - "13 331 job_m0d08 -12.228265 51.728341\n", - "2 319 job_m0_06 -12.799424 55.175799\n", - "14 332 job_m0d06 -12.799424 55.175799\n", - "3 320 job_m0_04 -13.172317 58.773123\n", - "15 333 job_m0d04 -13.172317 58.773123\n", - "4 321 job_m0_02 -13.377051 62.523502\n", - "16 334 job_m0d02 -13.377051 62.523502\n", - "17 335 job_0d0 -13.440000 66.430125\n", - "11 329 relax -13.440000 66.430125\n", - "5 322 job_0_0 -13.440000 66.430125\n", - "6 323 job_0_02 -13.384226 70.496180\n", - "18 336 job_0d02 -13.384226 70.496180\n", - "7 324 job_0_04 -13.229859 74.724856\n", - "19 337 job_0d04 -13.229859 74.724856\n", - "8 325 job_0_06 -12.994427 79.119342\n", - "20 338 job_0d06 -12.994427 79.119342\n", - "9 326 job_0_08 -12.693160 83.682826\n", - "21 339 job_0d08 -12.693160 83.682826\n", - "10 327 job_0_1 -12.339254 88.418496\n", - "22 340 job_0d1 -12.339254 88.418496" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = table.get_dataframe()\n", - "df = df.sort_values(by='volume')\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "bdc9e666-e861-4040-b7c6-625fad0bf659", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(df.volume, df.energy_tot);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b7659ea5-c319-4592-a52f-fa02a632011e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/eos/pyiron/pyiron_like_workflows.ipynb b/eos/pyiron/pyiron_like_workflows.ipynb deleted file mode 100644 index d81ada0..0000000 --- a/eos/pyiron/pyiron_like_workflows.ipynb +++ /dev/null @@ -1,1602 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "04fd3bae-2233-4bfd-968e-b9bbf29fbdee", - "metadata": {}, - "source": [ - "# Explore pyiron-like workflows" - ] - }, - { - "cell_type": "markdown", - "id": "2516cd60-2976-4a31-be17-d6ead55db1dc", - "metadata": {}, - "source": [ - "Aim: Explore how close the presently available features of the pyiron_workflow class allow us to write pyiron-like workflows" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "762d44f0-c4c5-4387-8a48-ebeb4859aad8", - "metadata": {}, - "outputs": [], - "source": [ - "%config IPCompleter.evaluation='unsafe'\n", - "\n", - "import matplotlib.pylab as plt\n", - "from pyiron_workflow.workflow import Workflow" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "1674329d-aa54-4d51-bb96-4426789d822b", - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "markdown", - "id": "47798e04-5765-4071-a991-f78d3879a594", - "metadata": {}, - "source": [ - "### Create structure and list available potentials" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "54e805cf-2975-4fb5-8216-6b1137e8b0d9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Al: [0. 0. 0.]\n", - "Al: [0. 2.025 2.025]\n", - "Al: [2.025 0. 2.025]\n", - "Al: [2.025 2.025 0. ]\n", - "tags: \n", - " indices: [0 0 0 0]\n", - "pbc: [ True True True]\n", - "cell: \n", - "Cell([4.05, 4.05, 4.05])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wf = Workflow('Lammps')\n", - "structure = wf.create.atomistics.Bulk('Al', cubic=True)\n", - "repeat = wf.create.lammps.Repeat(structure = structure, repeat_scalar=1)\n", - "strain = wf.create.lammps.ApplyStrain(structure=repeat, strain=0.0)\n", - "\n", - "strain.pull()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35525aed-95fd-4d46-a31a-dbb945a2b613", - "metadata": {}, - "outputs": [], - "source": [ - "structure.inputs.name = 'Fe'\n", - "# structure.inputs.cubic = False\n", - "wf.run();" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f62081d5-9fe0-49c5-8dc4-665aead02b79", - "metadata": {}, - "outputs": [], - "source": [ - "wf.repeat.plot3d()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90185040-31f7-4551-82e6-3b96adca6e5e", - "metadata": {}, - "outputs": [], - "source": [ - "wf.draw(depth=2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "94c90cc8-082c-4392-a2f3-d417611a72c0", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "wf = Workflow('Lammps')\n", - "structure = wf.create.atomistics.Bulk('Al', cubic=True)\n", - "repeat = wf.create.lammps.Repeat(structure = structure, repeat_scalar=3)\n", - "list_potentials = wf.create.lammps.ListPotentials(structure=repeat) \n", - "wf.run()\n", - "list_potentials.outputs.potentials.value[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7e54c3ba-20d0-498a-b031-c7abf8a0e3c1", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "wf = Workflow('Lammps')\n", - "structure = wf.create.atomistics.Bulk('Al', cubic=True)\n", - "repeat = wf.create.lammps.Repeat(structure = structure, repeat_scalar=3)\n", - "list_potentials = wf.create.lammps.ListPotentials(structure=repeat) \n", - "#wf.run()\n", - "#list_potentials.outputs.potentials.value[:5]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "721d6882-ea70-48c1-885a-c7f2314fdabb", - "metadata": {}, - "outputs": [ - { - "data": { - "application/json": { - "connected": true, - "fully_connected": false, - "inputs": { - "channels": { - "repeat_scalar": { - "connected": false, - "connections": [], - "label": "repeat_scalar", - "ready": true, - "value": "3" - }, - "structure": { - "connected": true, - "connections": [ - "bulk.structure" - ], - "label": "structure", - "ready": true, - "value": "None" - } - }, - "connected": true, - "fully_connected": false, - "label": "Inputs", - "ready": true - }, - "label": "repeat", - "outputs": { - "channels": { - "structure": { - "connected": true, - "connections": [ - "list_potentials.structure" - ], - "label": "structure", - "ready": false, - "value": "" - } - }, - "connected": true, - "fully_connected": true, - "label": "Outputs", - "ready": false - }, - "ready": true, - "signals": { - "input": { - "channels": { - "run": { - "callback": "run", - "connected": false, - "connections": [], - "label": "run" - } - }, - "connected": false, - "fully_connected": false, - "label": "InputSignals" - }, - "output": { - "channels": { - "ran": { - "connected": false, - "connections": [], - "label": "ran" - } - }, - "connected": false, - "fully_connected": false, - "label": "OutputSignals" - } - } - }, - "text/plain": [ - "NotData" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "repeat" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "de3fa9ae-183d-432c-b119-cf34616523b1", - "metadata": {}, - "outputs": [], - "source": [ - "wf = Workflow('Lammps')\n", - "structure = wf.create.atomistics.Bulk('Al', cubic=True)\n", - "repeat = wf.create.lammps.Repeat(structure = structure, repeat_scalar=3)\n", - "\n", - "lammps = wf.create.atomistic_codes.Lammps(structure=repeat, label='lammps')\n", - "wf.run()" - ] - }, - { - "cell_type": "markdown", - "id": "3e957298-f271-400b-9aeb-3014aa4854f7", - "metadata": {}, - "source": [ - "### MD over list of temperatures" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9cd9260a-825d-4c83-a81d-a18baad03be3", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "for T in [300, 600, 900]:\n", - " wf = Workflow('Lammps')\n", - " structure = wf.create.atomistics.Bulk('Al', cubic=True)\n", - " repeat = wf.create.lammps.Repeat(structure = structure, repeat_scalar=3)\n", - " \n", - " lammps = wf.create.atomistic_codes.Lammps(structure=repeat, label='lammps')\n", - " lammps.calc_select.md(temperature=T, n_ionic_steps=10_000)\n", - " \n", - " wf.run()\n", - " \n", - " plt.plot(wf.lammps.outputs.generic.value.energy_pot[2:], label=f'T={T}K')\n", - "plt.legend(); " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "e848e8fb-d421-44c6-a4cc-156d941af32e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volume\n", - "\n", - "energy_at_volume: EnergyAtVolume\n", - "\n", - "clusterenergy_at_volumeapply_strain\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "apply_strain: ApplyStrain\n", - "\n", - "\n", - "clusterenergy_at_volumeapply_strainInputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Inputs\n", - "\n", - "\n", - "clusterenergy_at_volumeapply_strainOutputs\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Outputs\n", - "\n", 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"clusterenergy_at_volumelammpsInputsshell__environment\n", - "\n", - "shell__environment: Optional\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumelammpsInputsshell__arguments\n", - "\n", - "shell__arguments: Optional\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumelammpsOutputsshell__output\n", - "\n", - "shell__output\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumelammpsOutputsgeneric\n", - "\n", - "generic\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumestructurelammpsInputsrun\n", - "\n", - "run\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumestructurelammpsOutputsran\n", - "\n", - "ran\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumestructurelammpsInputsstructure\n", - "\n", - "structure\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumepotentiallammpsInputsrun\n", - "\n", - "run\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumestructurelammpsOutputsran->clusterenergy_at_volumepotentiallammpsInputsrun\n", - "\n", - "\n", - 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"\n", - "clusterenergy_at_volumeinit_lammpslammpsInputsrun\n", - "\n", - "run\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumepotentiallammpsOutputsran->clusterenergy_at_volumeinit_lammpslammpsInputsrun\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumepotentiallammpsOutputspotential\n", - "\n", - "potential\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeinit_lammpslammpsInputspotential\n", - "\n", - "potential\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumepotentiallammpsOutputspotential->clusterenergy_at_volumeinit_lammpslammpsInputspotential\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumecalclammpsInputsrun\n", - "\n", - "run\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumecalclammpsOutputsran\n", - "\n", - "ran\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumecalclammpsOutputsran->clusterenergy_at_volumestructurelammpsInputsrun\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumecalclammpsOutputscalculator\n", - "\n", - "calculator\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeinit_lammpslammpsInputscalculator\n", - "\n", - "calculator\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumecalclammpsOutputscalculator->clusterenergy_at_volumeinit_lammpslammpsInputscalculator\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeinit_lammpslammpsOutputsran\n", - "\n", - "ran\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeinit_lammpslammpsInputsworking_directory\n", - "\n", - "working_directory\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeshelllammpsInputsrun\n", - "\n", - "run\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeinit_lammpslammpsOutputsran->clusterenergy_at_volumeshelllammpsInputsrun\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeinit_lammpslammpsOutputspath\n", - "\n", - "path\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeshelllammpsInputsworking_directory\n", - "\n", - "working_directory: str\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeinit_lammpslammpsOutputspath->clusterenergy_at_volumeshelllammpsInputsworking_directory\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeshelllammpsOutputsran\n", - "\n", - "ran\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeshelllammpsInputscommand\n", - "\n", - "command: str\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeshelllammpsInputsenvironment\n", - "\n", - "environment: Optional\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeshelllammpsInputsarguments\n", - "\n", - "arguments: Optional\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeparser_dump_filelammpsInputsrun\n", - "\n", - "run\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeshelllammpsOutputsran->clusterenergy_at_volumeparser_dump_filelammpsInputsrun\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeshelllammpsOutputsoutput\n", - "\n", - "output\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeshelllammpsOutputsdump\n", - "\n", - "dump\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeparser_dump_filelammpsInputsdump_file\n", - "\n", - "dump_file\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeshelllammpsOutputsdump->clusterenergy_at_volumeparser_dump_filelammpsInputsdump_file\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeshelllammpsOutputslog\n", - "\n", - "log\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeparser_log_filelammpsInputslog_file\n", - "\n", - "log_file\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeshelllammpsOutputslog->clusterenergy_at_volumeparser_log_filelammpsInputslog_file\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeparser_log_filelammpsInputsrun\n", - "\n", - "run\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeparser_log_filelammpsOutputsran\n", - "\n", - "ran\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumecollectlammpsInputsrun\n", - "\n", - "run\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeparser_log_filelammpsOutputsran->clusterenergy_at_volumecollectlammpsInputsrun\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeparser_log_filelammpsOutputslog\n", - "\n", - "log\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumecollectlammpsInputsout_log\n", - "\n", - "out_log\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeparser_log_filelammpsOutputslog->clusterenergy_at_volumecollectlammpsInputsout_log\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeparser_dump_filelammpsOutputsran\n", - "\n", - "ran\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeparser_dump_filelammpsOutputsran->clusterenergy_at_volumeparser_log_filelammpsInputsrun\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeparser_dump_filelammpsOutputsdump\n", - "\n", - "dump\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumecollectlammpsInputsout_dump\n", - "\n", - "out_dump\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumeparser_dump_filelammpsOutputsdump->clusterenergy_at_volumecollectlammpsInputsout_dump\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumecollectlammpsOutputsran\n", - "\n", - "ran\n", - "\n", - "\n", - "\n", - "\n", - "clusterenergy_at_volumecollectlammpsOutputsgeneric\n", - "\n", - "generic\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wf.draw(depth=2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c9309622-4351-4579-a514-6ac2c766dc12", - "metadata": {}, - "outputs": [], - "source": [ - "wf.lammps.draw()" - ] - }, - { - "cell_type": "markdown", - "id": "6bd036be-cde6-4447-90a4-ed147c5b54cc", - "metadata": { - "tags": [] - }, - "source": [ - "### Murnaghan (E-V) macro" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8b94f3d9-d490-4131-9e60-904dbe1e33ae", - "metadata": {}, - "outputs": [], - "source": [ - "# volumes = wf.create.standard.Linspace(0.9, 1.1, 11)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "dd4fb45e-1c25-4224-b23d-6f3d77544eb4", - "metadata": {}, - "outputs": [], - "source": [ - "@Workflow.wrap_as.macro_node()\n", - "def energy_at_volume(wf) -> None:\n", - " bulk = wf.create.atomistics.Bulk('Al', cubic=True)\n", - " repeat = wf.create.lammps.Repeat(structure = bulk, repeat_scalar=1)\n", - " apply_strain = wf.create.lammps.ApplyStrain(structure=repeat, strain=1)\n", - " \n", - " \n", - " lammps = wf.create.atomistic_codes.Lammps(structure=apply_strain, label='lammps')\n", - " \n", - " wf.inputs_map = {\n", - " \"bulk__name\": \"element\",\n", - " \"apply_strain__strain\": \"strain\",\n", - " \"lammps__potential__name\": \"potential\",\n", - " }\n", - " wf.outputs_map = {\"lammps__collect__generic\": \"generic\"}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "16135d5f-1ef3-4a20-adf7-431e75e24580", - "metadata": {}, - "outputs": [], - "source": [ - "wf.create.iter(energy_at_volume, strain=wf.create.numpy.linspace(-0.2, 0.2, 11))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "3050e5e5-4c68-4195-a665-0f5d4b0b78b0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "energy_pot = []\n", - "strain_lst = np.linspace(0.86, 2, 51) - 1\n", - "for strain in strain_lst:\n", - " wf = energy_at_volume(element='Al', strain=strain)\n", - " out = wf.run()\n", - "\n", - " energy_pot.append(out['lammps__generic'].energy_pot.iloc[0]) \n", - "\n", - "plt.plot(strain_lst, energy_pot);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7c476ec9-e35b-46fe-bca1-f7705aa205d2", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/eos/pyiron/quantumespresso.ipynb b/eos/pyiron/quantumespresso.ipynb deleted file mode 100644 index f200efa..0000000 --- a/eos/pyiron/quantumespresso.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.7"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os","metadata":{"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np","metadata":{"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"from ase.build import bulk\nfrom ase.io import write","metadata":{"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"from adis_tools.parsers import parse_pw","metadata":{"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"# Functions","metadata":{}},{"cell_type":"code","source":"def write_input(input_dict, working_directory=\".\"):\n filename = os.path.join(working_directory, 'input.pwi')\n os.makedirs(working_directory, exist_ok=True)\n write(\n filename=filename, \n images=input_dict[\"structure\"], \n Crystal=True, \n kpts=input_dict[\"kpts\"], \n input_data={\n 'CONTROL': {\n 'calculation': input_dict[\"calculation\"],\n },\n 'SYSTEM': {\n 'occupations': 'smearing',\n 'degauss': input_dict[\"smearing\"],\n }\n }, \n pseudopotentials=input_dict[\"pseudopotentials\"],\n tstress=True, \n tprnfor=True\n )","metadata":{"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"def collect_output(working_directory=\".\"):\n output = parse_pw(os.path.join(working_directory, 'pwscf.xml'))\n return {\n \"structure\": output['ase_structure'],\n \"energy\": output[\"energy\"],\n \"volume\": output['ase_structure'].get_volume(),\n }","metadata":{"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"def workflow(project, structure): \n # Structure optimization \n job_qe_minimize = pr.create.job.QEJob(job_name=\"qe_relax\")\n job_qe_minimize.input[\"calculation\"] = \"vc-relax\"\n job_qe_minimize.input.structure = structure\n job_qe_minimize.run()\n structure_opt = job_qe_minimize.output.structure\n\n # Energy Volume Curve \n energy_lst, volume_lst = [], []\n for i, strain in enumerate(np.linspace(0.9, 1.1, 5)):\n structure_strain = structure_opt.copy()\n structure_strain = structure.copy()\n structure_strain.set_cell(\n structure_strain.cell * strain**(1/3), \n scale_atoms=True\n )\n job_strain = pr.create.job.QEJob(\n job_name=\"job_strain_\" + str(i)\n )\n job_strain.input.structure = structure_strain\n job_strain.run(delete_existing_job=True)\n energy_lst.append(job_strain.output.energy)\n volume_lst.append(job_strain.output.volume)\n \n return {\"volume\": volume_lst, \"energy\": energy_lst}","metadata":{"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":"# Setup","metadata":{}},{"cell_type":"code","source":"from pyiron_base import Project","metadata":{"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"pr = Project(\"test\")\npr.remove_jobs(recursive=True, silently=True)","metadata":{"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"pr.create_job_class(\n class_name=\"QEJob\",\n write_input_funct=write_input,\n collect_output_funct=collect_output,\n default_input_dict={ # Default Parameter \n \"structure\": None, \n \"pseudopotentials\": {\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"}, \n \"kpts\": (3, 3, 3),\n \"calculation\": \"scf\",\n \"smearing\": 0.02,\n },\n executable_str=\"mpirun -np 1 pw.x -in input.pwi > output.pwo\",\n)","metadata":{"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":"# Workflow","metadata":{}},{"cell_type":"code","source":"job_workflow = pr.wrap_python_function(workflow)\njob_workflow.input.project = pr\njob_workflow.input.structure = bulk('Al', a=4.05, cubic=True)\njob_workflow.run()","metadata":{"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"The job workflowbc8cc7fbdf2b9679971e2ab088ec08f0 was saved and received the ID: 1\nThe job qe_relax was saved and received the ID: 2\nThe job job_strain_0 was saved and received the ID: 3\nThe job job_strain_1 was saved and received the ID: 4\nThe job job_strain_2 was saved and received the ID: 5\nThe job job_strain_3 was saved and received the ID: 6\nThe job job_strain_4 was saved and received the ID: 7\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Result","metadata":{}},{"cell_type":"code","source":"plt.plot(job_workflow.output.result[\"volume\"], job_workflow.output.result[\"energy\"])\nplt.xlabel(\"Volume\")\nplt.ylabel(\"Energy\")","metadata":{"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Energy')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/eos/aiida/files/Al.pbe-n-kjpaw_psl.1.0.0.UPF b/espresso/pseudo/Al.pbe-n-kjpaw_psl.1.0.0.UPF similarity index 100% rename from eos/aiida/files/Al.pbe-n-kjpaw_psl.1.0.0.UPF rename to espresso/pseudo/Al.pbe-n-kjpaw_psl.1.0.0.UPF diff --git a/jobflow.ipynb b/jobflow.ipynb new file mode 100644 index 0000000..567dd3d --- /dev/null +++ b/jobflow.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.11.8","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import subprocess\nimport os\nfrom pydantic import BaseModel, Field","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:39.230259Z","start_time":"2024-04-04T12:23:39.139154Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:39.812727Z","start_time":"2024-04-04T12:23:39.233298Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"from ase.build import bulk\nfrom ase.io import write","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.064521Z","start_time":"2024-04-04T12:23:39.815771Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"from adis_tools.parsers import parse_pw","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.342218Z","start_time":"2024-04-04T12:23:40.067769Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"# Functions","metadata":{}},{"cell_type":"code","source":"def generate_structures(structure, strain_lst): \n structure_lst = []\n for strain in strain_lst:\n structure_strain = structure.copy()\n structure_strain.set_cell(\n structure_strain.cell * strain**(1/3), \n scale_atoms=True\n )\n structure_lst.append(structure_strain)\n return structure_lst","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.349810Z","start_time":"2024-04-04T12:23:40.344659Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"def plot_energy_volume_curve(volume_lst, energy_lst):\n plt.plot(volume_lst, energy_lst)\n plt.xlabel(\"Volume\")\n plt.ylabel(\"Energy\")\n plt.savefig(\"evcurve.png\")","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.354972Z","start_time":"2024-04-04T12:23:40.351547Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"def write_input(input_dict, working_directory=\".\"):\n filename = os.path.join(working_directory, 'input.pwi')\n os.makedirs(working_directory, exist_ok=True)\n write(\n filename=filename, \n images=input_dict[\"structure\"], \n Crystal=True, \n kpts=input_dict[\"kpts\"], \n input_data={\n 'calculation': input_dict[\"calculation\"],\n 'occupations': 'smearing',\n 'degauss': input_dict[\"smearing\"],\n }, \n pseudopotentials=input_dict[\"pseudopotentials\"],\n tstress=True, \n tprnfor=True\n )","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.361062Z","start_time":"2024-04-04T12:23:40.356433Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"def collect_output(working_directory=\".\"):\n output = parse_pw(os.path.join(working_directory, 'pwscf.xml'))\n return {\n \"structure\": output['ase_structure'],\n \"energy\": output[\"energy\"],\n \"volume\": output['ase_structure'].get_volume(),\n }","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.365984Z","start_time":"2024-04-04T12:23:40.362667Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"from pymatgen.io.core import InputSet, InputGenerator\nfrom pymatgen.io.ase import MSONAtoms\nfrom typing import Any, Optional, Union\nQE_CMD= \"mpirun -np 1 pw.x -in input.pwi > output.pwo\"\ndef run_qe(qe_cmd=QE_CMD):\n subprocess.check_output(qe_cmd, shell=True, universal_newlines=True)\n\nclass QETaskDoc(BaseModel):\n structure: Optional[MSONAtoms] = Field(None, description=\"ASE structure\")\n energy: Optional[float] = Field(None, description=\"DFT energy in eV\")\n volume: Optional[float] = Field(None, description=\"volume in Angstrom^3\")\n \n @classmethod\n def from_directory(cls, working_directory):\n output=collect_output(working_directory=working_directory)\n # structure object needs to be serializable, i.e., we need an additional transformation\n return cls(structure=MSONAtoms(output[\"structure\"]), energy=output[\"energy\"], volume=output[\"volume\"])\n\nclass QEInputSet(InputSet):\n \"\"\"\n Writes an input based on an input_dict\n \"\"\"\n def __init__(self, input_dict):\n self.input_dict = input_dict\n\n def write_input(self, working_directory=\".\"):\n write_input(self.input_dict, working_directory=working_directory)\n\nfrom dataclasses import dataclass, field\n\n\n\n@dataclass\nclass QEInputGenerator(InputGenerator):\n pseudopotentials: dict = field(default_factory=lambda: {\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\n kpts: tuple = (3,3,3)\n calculation: str = \"vc-relax\"\n smearing: float = 0.02\n \n\n def get_input_set(self, structure) -> QEInputSet:\n\n input_dict={\"structure\":structure,\n \"pseudopotentials\":self.pseudopotentials, \n \"kpts\": self.kpts,\n \"calculation\": self.calculation,\n \"smearing\": self.smearing,\n }\n return QEInputSet(input_dict=input_dict)\n\n@dataclass\nclass QEInputStaticGenerator(QEInputGenerator):\n calculation: str = \"scf\"\n\n \ndef write_qe_input_set(structure, input_set_generator=QEInputGenerator(), working_directory=\".\"):\n qis = input_set_generator.get_input_set(structure=structure)\n qis.write_input(working_directory=working_directory)\n \n ","metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false},"ExecuteTime":{"end_time":"2024-04-04T12:23:40.992461Z","start_time":"2024-04-04T12:23:40.368632Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"from dataclasses import dataclass, field\nfrom jobflow import job, Maker\n\n\n@dataclass\nclass BaseQEMaker(Maker):\n \"\"\"\n Base QE job maker.\n\n Parameters\n ----------\n name : str\n The job name.\n input_set_generator : .QEInputGenerator\n A generator used to make the input set.\n \"\"\"\n\n name: str = \"base qe job\"\n input_set_generator: QEInputGenerator = field(default_factory=QEInputGenerator)\n\n @job(output_schema=QETaskDoc)\n def make(\n self, structure\n ) -> QETaskDoc:\n \"\"\"\n Run a QE calculation.\n\n Parameters\n ----------\n structure : MSONAtoms|Atoms\n An Atoms or MSONAtoms object.\n \n Returns\n -------\n Output of a QE calculation\n \"\"\"\n # copy previous inputs\n\n # write qe input files\n write_qe_input_set(\n structure=structure, input_set_generator=self.input_set_generator)\n\n # qe\n run_qe()\n\n # parse qe outputs\n task_doc=QETaskDoc.from_directory(\".\")\n \n return task_doc\n\n@dataclass\nclass StaticQEMaker(BaseQEMaker):\n \"\"\"\n Base QE job maker.\n\n Parameters\n ----------\n name : str\n The job name.\n input_set_generator : .QEInputGenerator\n A generator used to make the input set.\n \"\"\"\n\n name: str = \"static qe job\"\n input_set_generator: QEInputGenerator = field(default_factory=QEInputStaticGenerator)\n\n\n\nfrom jobflow import job, Response, Flow, run_locally\n\n@job\ndef get_ev_curve(structure, strain_lst):\n structures=generate_structures(structure,strain_lst=strain_lst)\n jobs = []\n volumes = []\n energies = []\n for istructure in range(len(strain_lst)):\n new_job = StaticQEMaker().make(structures[istructure])\n jobs.append(new_job)\n volumes.append(new_job.output.volume)\n energies.append(new_job.output.energy)\n return Response(replace=Flow(jobs, output={\"energies\": energies, \"volumes\": volumes}))\n \n@job\ndef plot_energy_volume_curve_job(volume_lst, energy_lst):\n plot_energy_volume_curve(volume_lst=volume_lst, energy_lst=energy_lst)\n\nstructure = bulk('Al', a=4.15, cubic=True)\nrelax = BaseQEMaker().make(structure=MSONAtoms(structure))\nev_curve = get_ev_curve(relax.output.structure, strain_lst=np.linspace(0.9, 1.1, 5))\nplot = plot_energy_volume_curve_job(volume_lst=ev_curve.output[\"volumes\"], energy_lst=ev_curve.output[\"energies\"])\njobs = [relax, ev_curve, plot]\nrun_locally(Flow(jobs), create_folders=True)","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:26:08.108635Z","start_time":"2024-04-04T12:24:48.120617Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"2024-04-04 15:55:32,815 INFO Started executing jobs locally\n2024-04-04 15:55:32,892 INFO Starting job - base qe job (72ab2547-8a66-4dd0-a95a-b6255a668cd8)\n","output_type":"stream"},{"name":"stderr","text":"[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00139] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:56:37,395 INFO Finished job - base qe job (72ab2547-8a66-4dd0-a95a-b6255a668cd8)\n2024-04-04 15:56:37,396 INFO Starting job - get_ev_curve (87ad6b9b-9f34-463e-b705-1ab7cdcf9aee)\n2024-04-04 15:56:37,404 INFO Finished job - get_ev_curve (87ad6b9b-9f34-463e-b705-1ab7cdcf9aee)\n2024-04-04 15:56:37,406 INFO Starting job - static qe job (b0759785-155b-4e79-9b72-c626067d81e1)\n","output_type":"stream"},{"name":"stderr","text":"[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00149] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:56:45,563 INFO Finished job - static qe job (b0759785-155b-4e79-9b72-c626067d81e1)\n2024-04-04 15:56:45,564 INFO Starting job - static qe job (514079e9-d6ae-4369-8b80-bf8ca7860540)\n","output_type":"stream"},{"name":"stderr","text":"Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00159] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:56:53,979 INFO Finished job - static qe job (514079e9-d6ae-4369-8b80-bf8ca7860540)\n2024-04-04 15:56:53,980 INFO Starting job - static qe job (6e3c5a28-4edb-4244-9e35-0b8733b946c4)\n","output_type":"stream"},{"name":"stderr","text":"[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00169] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:57:03,600 INFO Finished job - static qe job (6e3c5a28-4edb-4244-9e35-0b8733b946c4)\n2024-04-04 15:57:03,600 INFO Starting job - static qe job (980b72a5-fbea-445c-9075-a880322c8261)\n","output_type":"stream"},{"name":"stderr","text":"Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00179] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:57:14,907 INFO Finished job - static qe job (980b72a5-fbea-445c-9075-a880322c8261)\n2024-04-04 15:57:14,908 INFO Starting job - static qe job (a44a935f-6bd2-4279-bbcb-53d5ed890a99)\n","output_type":"stream"},{"name":"stderr","text":"Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00189] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:57:27,174 INFO Finished job - static qe job (a44a935f-6bd2-4279-bbcb-53d5ed890a99)\n2024-04-04 15:57:27,174 INFO Starting job - store_inputs (87ad6b9b-9f34-463e-b705-1ab7cdcf9aee, 2)\n2024-04-04 15:57:27,176 INFO Finished job - store_inputs (87ad6b9b-9f34-463e-b705-1ab7cdcf9aee, 2)\n2024-04-04 15:57:27,176 INFO Starting job - plot_energy_volume_curve_job (9987ab9f-1ae9-4172-8e31-b2c9920d4791)\n2024-04-04 15:57:27,256 INFO Finished job - plot_energy_volume_curve_job (9987ab9f-1ae9-4172-8e31-b2c9920d4791)\n2024-04-04 15:57:27,256 INFO Finished executing jobs locally\n","output_type":"stream"},{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"{'72ab2547-8a66-4dd0-a95a-b6255a668cd8': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[4.045218941837687, 4.045218941837687, 4.045218941837687]), energy=-1074.9365272693506, volume=66.1951387021735), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '87ad6b9b-9f34-463e-b705-1ab7cdcf9aee': {1: Response(output=None, detour=None, addition=None, replace=Flow(name='Flow', uuid='0e46d480-7699-4e03-889b-b4ba65fe3d38')\n 1. Job(name='static qe job', uuid='b0759785-155b-4e79-9b72-c626067d81e1')\n 2. Job(name='static qe job', uuid='514079e9-d6ae-4369-8b80-bf8ca7860540')\n 3. Job(name='static qe job', uuid='6e3c5a28-4edb-4244-9e35-0b8733b946c4')\n 4. Job(name='static qe job', uuid='980b72a5-fbea-445c-9075-a880322c8261')\n 5. Job(name='static qe job', uuid='a44a935f-6bd2-4279-bbcb-53d5ed890a99')\n 6. Job(name='store_inputs', uuid='87ad6b9b-9f34-463e-b705-1ab7cdcf9aee'), stored_data=None, stop_children=False, stop_jobflow=False),\n 2: Response(output={'energies': [OutputReference(b0759785-155b-4e79-9b72-c626067d81e1, .energy), OutputReference(514079e9-d6ae-4369-8b80-bf8ca7860540, .energy), OutputReference(6e3c5a28-4edb-4244-9e35-0b8733b946c4, .energy), OutputReference(980b72a5-fbea-445c-9075-a880322c8261, .energy), OutputReference(a44a935f-6bd2-4279-bbcb-53d5ed890a99, .energy)], 'volumes': [OutputReference(b0759785-155b-4e79-9b72-c626067d81e1, .volume), OutputReference(514079e9-d6ae-4369-8b80-bf8ca7860540, .volume), OutputReference(6e3c5a28-4edb-4244-9e35-0b8733b946c4, .volume), OutputReference(980b72a5-fbea-445c-9075-a880322c8261, .volume), OutputReference(a44a935f-6bd2-4279-bbcb-53d5ed890a99, .volume)]}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n 'b0759785-155b-4e79-9b72-c626067d81e1': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[3.9056159296787105, 3.9056159296787105, 3.9056159296787105]), energy=-1074.8451830762128, volume=59.575624050752516), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '514079e9-d6ae-4369-8b80-bf8ca7860540': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[3.9766426435887574, 3.9766426435887574, 3.9766426435887574]), energy=-1074.9158947387848, volume=62.88538094246082), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '6e3c5a28-4edb-4244-9e35-0b8733b946c4': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[4.045218924156295, 4.045218924156295, 4.045218924156295]), energy=-1074.936525208987, volume=66.19513783416937), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '980b72a5-fbea-445c-9075-a880322c8261': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[4.111545777030954, 4.111545777030954, 4.111545777030954]), energy=-1074.9194989203452, volume=69.50489472587755), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n 'a44a935f-6bd2-4279-bbcb-53d5ed890a99': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[4.175799058074337, 4.175799058074337, 4.175799058074337]), energy=-1074.8741797823543, volume=72.81465161758611), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '9987ab9f-1ae9-4172-8e31-b2c9920d4791': {1: Response(output=None, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)}}"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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n2cD1enMWlwB7MjiYiIOBynmLPUoUMHBg0axLhx40hISCAhIYFx48YxZMiQct+Ei4yMZOHChQD4+/tz5ZVXMnHiRFasWMHevXuZNWsWH374ISNGjABKT+1NnDiRN954g3nz5rFr1y6mTJnCjh07uPfee015rfY2tm9LAOasPUBeYbHJaURERByPqSNLF+OTTz7hoYceYsCAAQAMGzaMN998s9w2KSkpZGdnl92fM2cOkyZN4o477uDEiROEh4fz/PPP88ADD5Rt8/DDD5Ofn8+ECRM4ceIEMTExLFu2jNatW9vnhZnsmsggWjTyYf+JPBZuOsQdv11sV0REREo5xTpLjs7Z1ln6o/d/3cuzX2+nbZAf3024otJJ8yIiInVFnVpnSWrXqB5h+Hq4svPIaX7ddczsOCIiIg5FZUnw93JnVI/StaJmrkwzN4yIiIiDUVkSAO7uE4HFAj/uOMLeY7lmxxEREXEYKksCQMtAX65uX7pK+uxVaeaGERERcSAqS1JmbN8IAOauP0BOfpG5YURERByEypKU6dcmkLZBfuQWljB3/UGz44iIiDgElSUpY7FYGPPb6NLsVWmU2LSqhIiIiMqSlDOiazOs3u7sP5HHjzuOmB1HRETEdCpLUo6Phxu3XnZ2GYG9JqcRERExn8qSnCMuNhwXC6zafZwdGTlmxxERETGVypKcI6yhDwOjQgCYpUUqRUSknlNZkgqN7dsSgIWbDpGVW2hyGhEREfOoLEmFLotoSFSoPwXFNj5bt9/sOCIiIqZRWZIKWSyWstGlj1bvo6jEZnIiERERc6gsyXkNjWlKoJ8H6dn5LN2WYXYcERERU6gsyXl5urlye69wAGZqoreIiNRTKktSqTt7t8Dd1cKGfVlsPnjS7DgiIiJ2p7IklQpq4MWQ6FBAo0siIlI/qSzJBY397XpxX28+zJGcfHPDiIiI2JnKklxQdFgA3cMbUlRi8PEaLSMgIiL1i8qSVMnZ0aVP1+yjoLjE3DAiIiJ2pLIkVTIwKoSmVi+OnS5kcVK62XFERETsRmVJqsTd1YW42LPLCOzFMAyTE4mIiNiHypJU2W2XtcDL3YVth3NYl5ZldhwRERG7UFmSKmvo68GIrs2A0tElERGR+kBlSS7KmD6l14tbui2Dg1l5JqcRERGpfSpLclHahzSgT+vG2IzSC+yKiIjUdSpLctHG9i0dXfps7X7yCotNTiMiIlK7VJbkol0TGUSLRj7k5BezYOMhs+OIiIjUKpUluWiuLhbu7hMBwKxVaVpGQERE6jSVJamWUT3C8PVwZdeR0/y665jZcURERGqNypJUi7+XO6N6NAdg5so0c8OIiIjUIpUlqba7+0RgscCPO46w91iu2XFERERqhcqSVFvLQF+ubh8EwOxVaeaGERERqSUqS3JJxvaNAGDu+gPk5BeZG0ZERKQWqCzJJenXJpC2QX7kFpYwd/1Bs+OIiIjUOJUluSQWi4Uxv40uzV6VRolNywiIiEjdorIkl+ymrmFYvd3ZfyKPH3ccMTuOiIhIjVJZkkvm7eHKrT3PLiOw1+Q0IiIiNUtlSWrEXbERuLpYWLX7ODsycsyOIyIiUmNUlqRGNAvwZmBUMACztEiliIjUISpLUmPG9m0JwMJNhziRW2hyGhERkZqhsiQ1pkd4Qzo186eg2MZna/ebHUdERKRGqCxJjbFYLIztUzq69NHqfRSV2ExOJCIiculUlqRGDYlpSqCfBxk5+cRvzTA7joiIOLmth7KZumgbeYXFpmVQWZIa5enmyu29wgEtIyAiIpemoLiEx+YmMWtVGi9+u8O0HCpLUuPu7N0Cd1cLG/efJOnASbPjiIiIk3rjh53syDhFY18PHrq2rWk5VJakxgU18GJIdCig0SUREameTfuzeHvFbgCeH9GJxn6epmVRWZJaMfa368V9syWdIzn55oYRERGnkl9UwqNzk7AZcGOXUAZ1ampqHpUlqRXRYQF0D29IUYnBxwn7zI4jIiJO5JWlKew5mktQA0+eGRZldhyVJak9Z0eXPlmzn4LiEnPDiIiIU1i79wTv/zaFY/rNnQnw8TA5kcqS1KKBUSE0tXpxPLeQxUnpZscREREHl1dYzMR5SRgGjO4RxjWRwWZHAlSWpBa5u7oQF/u/ZQQMwzA5kYiIOLLp3+5g3/E8Qq1ePDmko9lxyqgsSa267bIWeLm7sO1wDuvSssyOIyIiDmrlrmN8uLp0jutLI2Pw93I3OdH/qCxJrWro68GIrs0ALSMgIiIVO5VfxOPzNgOla/X1axtocqLyVJak1o357XpxS7dlcDArz+Q0IiLiaJ7/JplDJ8/QopEPkwZ3MDvOOVSWpNa1D2lA3zaNsRmlF9gVERE5a/mOI8xZdwCLBV4eGY2vp5vZkc6hsiR2Mfa30aXP1u439WKIIiLiOLLzivj7gtLTb2P7tKRXq8YmJ6qYypLYxTWRQYQ39iEnv5gFGw+ZHUdERBzA1MXbyMwpoFWgL48Pam92nPNSWRK7cHGxcHdsBACzVqVpGQERkXpu6bYMFm46hIsFXhkdg5e7q9mRzktlSexmVI8wfD1c2XXkNL/sPGZ2HBERMcmJ3EL+sXALAPdf2ZpuLRqanKhyKktiNw283BnVozmgZQREROqzKV9u5djpQtoHN+Dh/m3NjnNBKktiV3f3icBigeUpR9lz9LTZcURExM4WJx3mmy3puLlYmDE6Bk83xz39dpbTlKWsrCzi4uKwWq1YrVbi4uI4efJkpfucPn2a8ePHExYWhre3Nx06dODtt98ut01GRgZxcXGEhITg6+tLt27dmDdvXi2+kvqtZaAvV7cPAmD2qjRzw4iIiF0dOZXPlK+2AvDXq9vQqZnV5ERV4zRl6fbbbycxMZH4+Hji4+NJTEwkLi6u0n0mTJhAfHw8H3/8McnJyUyYMIEHH3yQr776qmybuLg4UlJSWLRoEVu2bOGmm27illtuYdOmTbX9kuqtsX0jAJi34SA5+UXmhhEREbswDIPJC7ZyMq+IqFB/xl/TxuxIVeYUZSk5OZn4+Hj++9//EhsbS2xsLO+99x5ff/01KSkp591v9erV3H333Vx11VVERETwpz/9iZiYGNavX19umwcffJCePXvSqlUrnnzySQICAti4caM9Xlq91K9NIG2D/MgtLOGLdQfMjiMiInYwf+Mhvk/OxN219PSbu6tTVBDAScrS6tWrsVqt9OrVq+yx3r17Y7VaWbVq1Xn369evH4sWLeLQoUMYhsHy5ctJTU1l4MCB5bb5/PPPOXHiBDabjTlz5lBQUMBVV1113uctKCggJyen3E2qzmKxMOa30aXZq9MosWkZARGRuiw9+wzPLN4GwMP92xEZ4m9yoovjFGUpIyODoKCgcx4PCgoiIyPjvPu98cYbdOzYkbCwMDw8PBg0aBBvvfUW/fr1K9vm888/p7i4mMaNG+Pp6cn999/PwoULad269Xmfd9q0aWVzp6xWK82bN7+0F1gP3dQ1DKu3OwdOnOGH5Eyz44iISC0xDIPH523mVH4xMc0DuP+KVmZHumimlqWpU6disVgqvZ09ZWaxWM7Z3zCMCh8/64033iAhIYFFixaxYcMGZsyYwV/+8he+//77sm2efPJJsrKy+P7771m/fj2PPPIIo0aNYsuWLed93kmTJpGdnV12O3BAp5IulreHK7f2PLuMQJq5YUREpNZ8tvYAv+w8hqebCzNGxeDmRKffzrIYJi6lfOzYMY4dq3xxwoiICD799FMeeeSRc779FhAQwGuvvcbYsWPP2e/MmTNYrVYWLlzIDTfcUPb4fffdx8GDB4mPj2f37t20adOGrVu3EhUVVbZN//79adOmDe+8806VXkdOTg5Wq5Xs7Gz8/Z1raNFMh06e4YqXllNiM4h/+HKnG5YVEZHKHTiRx6DXfya3sIQnb+jAfZc71qhSVT+/Tb20b2BgIIGBgRfcLjY2luzsbNauXUvPnj0BWLNmDdnZ2fTp06fCfYqKiigqKsLFpXyDdXV1xWazAZCXlwdQ6TZSe5oFeDMwKpglWzKYtTKN6TdHmx1JRERqiM1mMHFeErmFJfSMaMTYvi3NjlRtTjEW1qFDBwYNGsS4ceNISEggISGBcePGMWTIENq3/9+F9yIjI1m4cCEA/v7+XHnllUycOJEVK1awd+9eZs2axYcffsiIESPKtm/Tpg33338/a9euZffu3cyYMYNly5YxfPhwM15qvXP2X56Fmw5xIrfQ5DQiIlJTPlydRsKeE3i7u/LyqGhcXc4/bcbROUVZAvjkk0/o3LkzAwYMYMCAAURHR/PRRx+V2yYlJYXs7Oyy+3PmzOGyyy7jjjvuoGPHjkyfPp3nn3+eBx54AAB3d3eWLFlCkyZNGDp0KNHR0Xz44YfMnj2b66+/3q6vr77qEd6QTs38KSi28dna/WbHERGRGrD3WC7T43cAMPn6SMIb+5qc6NKYOmeprtCcpUszf8NBHp2bRIi/F788cbVTrb0hIiLlldgMRr+7mg37sujbpjEf3dMLFwcdVarq57c+lcR0Q2KaEujnSUZOPvFbz78UhIiIOL7//rKHDfuy8PN046WRMQ5blC6GypKYztPNlTt6tQBg5sq9JqcREZHq2pl5ihnLUgGYMqQDzQK8TU5UM1SWxCHc0bsF7q4WNu4/SdKBk2bHERGRi1RcYuPRuUkUFtu4un0TRveoOws2qyyJQwhq4MXQ6FBAo0siIs7o7RW72XwwG38vN6bfHF3potHORmVJHMbZZQS+2ZLOkZx8k9OIiEhVbT+cwxs/7gTgmRujCPb3MjlRzVJZEofROcxK9/CGFJUYfJywz+w4IiJSBYXFNh75IpGiEoOBUcEM79LM7Eg1TmVJHMrYvhEAfLJmP/lFJeaGERGRC/r3jzvZkXGKRr4ePD+ic506/XaWypI4lIFRITS1enE8t5DFSYfNjiMiIpVIOnCSt1bsBuC54Z0I9PM0OVHtUFkSh+Lu6kJcbDgAM1emoTVTRUQcU35RCY/OTaLEZjA0JpTrOzc1O1KtUVkSh3PbZS3wcndhe3oOa/eeMDuOiIhU4NVlqew6cpomDTz557Aos+PUKpUlcTgNfT0Y0bV0guDMlWnmhhERkXOsTzvBe7/sAWDaiM409PUwOVHtqlZZys3NrekcIuWM6VO6jMB32zM4cCLP5DQiInJWXmExj81NwjDg5m5h9O8YbHakWletshQcHMw999zDr7/+WtN5RABoH9KAvm0aYzPgIy0jICLiMF6KTyHteB4h/l48NbSj2XHsolpl6bPPPiM7O5trr72Wdu3aMX36dA4f1jeXpGaN/W10ac7a/eQVFpucRkREVu0+xqxVaQC8ODIaq7e7uYHspFplaejQocyfP5/Dhw/z5z//mc8++4zw8HCGDBnCggULKC7WB5tcumsigwhv7ENOfjELNh4yO46ISL12uqCYx+dtBuD2Xi24sl0TkxPZzyVN8G7cuDETJkwgKSmJV199le+//56RI0cSGhrKU089RV6e5ppI9bm4WLg7NgKAWau0jICIiJme/yaZg1lnCGvozeTrO5gdx64uqSxlZGTw0ksv0aFDB/7+978zcuRIfvjhB1577TUWLlzI8OHDayim1FejeoTh5+nGriOn+WXnMbPjiIjUSz+lHuWztfsBeHlkDH6ebiYnsq9qvdoFCxYwc+ZMli5dSseOHfnrX//KnXfeSUBAQNk2Xbp0oWvXrjWVU+qpBl7ujOwexqxVacxcuZcr6tGwr4iII8g+U8QTv51+G9MngtjWjU1OZH/VGlkaO3YsoaGhrFy5ksTERMaPH1+uKAG0atWKf/zjHzWRUeq5MX0isFhgecpR9hw9bXYcEZF65ZnF28jIyadloC9PDIo0O44pqjWylJ6ejo+PT6XbeHt78/TTT1crlMjvRQT6ck37IH7YcYTZq9J45sZOZkcSEakXlm3PZMHGQ7hY4JVR0Xh7uJodyRTVGlkqLi4mJyfnnNupU6coLCys6YwijO1buozAvA0HyckvMjmNiEjdl5VbyKQFWwAYd3kruoc3MjmReapVlgICAmjYsOE5t4CAALy9vQkPD+fpp5/GZrPVdF6pp/q2aUy7YD9yC0v4Yt0Bs+OIiNR5U77ayrHTBbQN8mPCde3MjmOqapWlWbNmERoayuTJk/nyyy9ZuHAhkydPplmzZrz99tv86U9/4o033mD69Ok1nVfqKYvFUnYJlNmr0yixaRkBEZHa8s3mdL7enI6ri4UZo2Pwcq+fp9/OqtacpdmzZzNjxgxGjx5d9tiwYcPo3Lkz7777Lj/88AMtWrTg+eefZ/LkyTUWVuq3EV2b8WL8Dg6cOMMPyZkMiAoxO5KISJ1z9FQBT35Zevrtr1e1JjoswNxADqBaI0urV6+ucFmArl27snr1agD69evH/v37Ly2dyO94e7hya8/mAMxcmWZuGBGROsgwDP6xcAtZeUV0bOrP+Gvamh3JIVSrLIWFhfH++++f8/j7779P8+alH2bHjx+nYcOGl5ZO5A/uio3A1cXC6j3HSU7PMTuOiEid8mXiIb7bnom7a+npNw+3S1q7us6o1mm4V155hVGjRvHtt99y2WWXYbFYWLduHTt27GDevHkArFu3jltuuaVGw4o0C/BmYFQwS7ZkMGtlGi+OjDY7kohInZCRnc/TX20D4G/XtqVDU3+TEzkOi1HNC27t27ePd955h5SUFAzDIDIykvvvv5+IiIgajuj4cnJysFqtZGdn4++vP67ati7tBKPeWY2nmwurJ11LI18PsyOJiDg1wzAYM3MdP6UeJSbMyvw/98HNte6PKlX18/uiR5aKiooYMGAA7777LtOmTbukkCLV0SO8IZ2a+bP1UA6frd3PX69uY3YkERGn9vm6A/yUehQPNxdmjI6pF0XpYlz00XB3d2fr1q1YLJbayCNyQRaLhbG/LSPw0ep9FJVoPS8Rkeo6mJXHc98kA/DYgHa0CWpgciLHU63qeNddd1U4wVvEXobENCXQz5OMnHy+3ZphdhwREadksxk8Pm8zpwuK6RHekHv7tTI7kkOq1gTvwsJC/vvf/7Js2TJ69OiBr69vuZ+/+uqrNRJO5Hw83Vy5o1cL/vXDTmau3MuwmFCzI4mIOJ2P1+xj1e7jeLm78PKoGFxddNaoItUqS1u3bqVbt24ApKamlvuZTs+JvdzRuwVvrdjFpv0nSTxwki7NA8yOJCLiNNKO5TJtyQ4AJg3uQMtA3wvsUX9VqywtX768pnOIXLSgBl4MjQ5lwaZDzFq5l9dvPXehVBEROVeJzWDivCTOFJUQ26oxcb3DzY7k0C5puvuuXbtYunQpZ86cAUq/eihiT2P7lk70/mZLOkdy8k1OIyLiHGau3Mu6tCx8PVx5aWQ0Ljr9VqlqlaXjx49z7bXX0q5dO66//nrS09MBuO+++3j00UdrNKBIZTqHWekR3pCiEoOPE/aZHUdExOHtOnKKl5amAPDkkI40b+RjciLHV62yNGHCBNzd3dm/fz8+Pv87yLfccgvx8fE1Fk6kKs6OLn2yZj/5RSUmpxERcVzFJTYe/SKJwmIbV7Zrwq2XNTc7klOoVln67rvvePHFFwkLCyv3eNu2bdm3T/91L/Y1MCqYUKsXx3MLWZx02Ow4IiIO692f95B0MJsGXm5Mv7mzvpRVRdUqS7m5ueVGlM46duwYnp6elxxK5GK4uboQFxsBwMyVaZo7JyJSgeT0HF7/vvQb7FOHRtHU6m1yIudRrbJ0xRVX8OGHH5bdt1gs2Gw2Xn75Za6++uoaCydSVbf1bI6Xuwvb03NYu/eE2XFERBxKYXHp6beiEoP+HYK5qVszsyM5lWotHfDyyy9z1VVXsX79egoLC3n88cfZtm0bJ06cYOXKlTWdUeSCAnw8GNG1GZ+tPcDMlWn0atXY7EgiIg7jzeW72J6eQ4CPOy/c1Emn3y5StUaWOnbsyObNm+nZsyfXXXcdubm53HTTTWzatInWrVvXdEaRKhnz2/XivtuewYETeSanERFxDFsOZvN/y3cB8NzwTgQ18DI5kfOp1sgSQEhICM8880xNZhG5JO1DGtC3TWNW7jrORwn7mHx9B7MjiYiYqqC4hEfnJlJiM7ghuilDonVpqOqodlk6efIka9eu5ciRI9hs5a/6ftddd11yMJHqGNunJSt3HWfO2v083L8tPh7V/hMXEXF6ry3bSWrmaQL9PHj2xk5mx3Fa1fokWbx4MXfccQe5ubk0aNCg3LlPi8WisiSmuSYyiPDGPuw7nsf8jYe0hL+I1Fsb9mXxn593A/DCiM408vUwOZHzqtacpUcffZR77rmHU6dOcfLkSbKysspuJ07om0hiHhcXC3f/tozArJV7sdm0jICI1D9nCkt4bG4SNgNu6tqMAVEhZkdyatUqS4cOHeKhhx6qcK0lEbON6hGGn6cbu4/m8suuY2bHERGxu5eW7mDvsVyC/T15emiU2XGcXrXK0sCBA1m/fn1NZxGpEQ283BnZvXR1+Zkr95qcRkTEvhL2HGfmyjQAXrw5GquPu7mB6oBqzVm64YYbmDhxItu3b6dz5864u5f/BzFs2LAaCSdSXWP6RDB7dRorUo6y++hpWjfxMzuSiEityy0oZuK8JABuvaw5V7UPMjlR3WAxqnFtCBeX8w9IWSwWSkrq18VMc3JysFqtZGdn4+/vb3Yc+c29s9bxw44j3BUbzj/1LRARqQf+sXALn6zZT7MAb+IfvpwGXhpVqkxVP7+rdRrOZrOd91bfipI4rrF9SxepnLfhINlnikxOIyJSu37ZeZRP1uwH4OWR0SpKNeiiytL1119PdnZ22f3nn3+ekydPlt0/fvw4HTt2rLFwIpeib5vGtAv2I6+whLnrD5gdR0Sk1uTkF/H4vM0A3B0bTp82gSYnqlsuqiwtXbqUgoKCsvsvvvhiuaUCiouLSUlJqbl0IpfAYrGUXQJl9uo0SrSMgIjUUc8u3k56dj4RjX14YnCk2XHqnIsqS3+c3lSN6U4idjWiazMCfNw5cOIMPyRnmh1HRKTG/ZCcydwNB7FY4JVRMbpyQS2o1pwlEWfh7eHKrZe1ACj7Kq2ISF2RlVvI3xdsAeC+fi3pEdHI5ER100WVJYvFUu7SJmcfE3Fkd8WG4+piYfWe4ySn55gdR0Skxjy9aBtHTxXQuokvjw5ob3acOuuixuoMw2DMmDF4enoCkJ+fzwMPPICvry9AuflMIo4iNMCbQVEhfLMlnVkr03hxZLTZkURELtm3W9JZlHQYFwvMGN0FL3dXsyPVWRdVlu6+++5y9++8885zttFFdMURje0bwTdb0vky8RBPDI7UBSVFxKkdO13AP77cCsCfr2pNl+YB5gaq4y6qLM2cObO2cojUqu7hDenUzJ+th3L4bO1+/np1G7MjiYhUi2EYPLlwKydyC4kMacBD17Y1O1KdpwneUi9YLBbG/raMwEer91FUYjM5kYhI9SxKOkz8tgzcXCzMGB2Dp5tOv9U2lSWpN4bENCXQz5OMnHy+3ZphdhwRkYuWmZPPU19tA+Cha9sSFWo1OVH9oLIk9Yanmyt39Dq7jMBek9OIiFwcwzCYtGAL2WeK6NzMyp+vam12pHrDacpSVlYWcXFxWK1WrFYrcXFx5S61UpHMzEzGjBlDaGgoPj4+DBo0iJ07d5bbpqCggAcffJDAwEB8fX0ZNmwYBw8erMVXIma6o3cL3F0tbNp/ksQDJ82OIyJSZXPXH+THHUfwcHVhxugY3F2d5iPc6TnNkb799ttJTEwkPj6e+Ph4EhMTiYuLO+/2hmEwfPhw9uzZw1dffcWmTZsIDw+nf//+5Obmlm338MMPs3DhQubMmcOvv/7K6dOnGTJkiC4IXEcFNfBiaHQooNElEXEeh06e4Z9fbwfgkQHtaBfcwORE9YvFcIJrliQnJ9OxY0cSEhLo1asXAAkJCcTGxrJjxw7atz93Ia7U1FTat2/P1q1biYqKAqCkpISgoCBefPFF7rvvPrKzs2nSpAkfffQRt9xyCwCHDx+mefPmLFmyhIEDB1YpX05ODlarlezsbPz9/WvoVUtt2XIwm6Fv/oqbi4WVf7+GYH8vsyOJiJyXYRjEvb+WX3cdo1uLAOY+0AdXFy0IXROq+vntFCNLq1evxmq1lhUlgN69e2O1Wlm1alWF+5xdINPL638fhK6urnh4ePDrr78CsGHDBoqKihgwYEDZNqGhoXTq1Om8zyvOr3OYlR7hDSm2GXycsM/sOCIilfp4zX5+3XUML3cXXhkVo6JkAqcoSxkZGQQFBZ3zeFBQEBkZFX+rKTIykvDwcCZNmkRWVhaFhYVMnz6djIwM0tPTy57Xw8ODhg0blts3ODj4vM8LpUUsJyen3E2cy9i+pcsIfLpmP/lFOuUqIo5p//E8pi1JBuDxgZG0auJncqL6ydSyNHXq1LLrzZ3vtn79eqDia9AZhnHea9O5u7szf/58UlNTadSoET4+PqxYsYLBgwfj6lr5mhSVPS/AtGnTyiaaW61WmjdvfhGvWhzBwKhgQq1eHM8tZFHSYbPjiIicw2YzeGxeEnmFJfRq2YgxfSLMjlRvmVqWxo8fT3JycqW3Tp06ERISQmZm5jn7Hz16lODg4PM+f/fu3UlMTOTkyZOkp6cTHx/P8ePHadmydFQhJCSEwsJCsrKyyu135MiRSp930qRJZGdnl90OHDhQzSMgZnFzdSEuNgKAmSvTcIKpeyJSz8xclcbavSfw8XDl5ZExuOj0m2ku6nInNS0wMJDAwMALbhcbG0t2djZr166lZ8+eAKxZs4bs7Gz69Olzwf2t1tJFu3bu3Mn69et59tlngdIy5e7uzrJlyxg9ejQA6enpbN26lZdeeum8z+fp6Vl2MWFxXrf1bM6/fkglOT2HNXtP0LtVY7MjiYgAsPvoaV6K3wHAP27oQIvGPiYnqt+cYs5Shw4dGDRoEOPGjSMhIYGEhATGjRvHkCFDyn0TLjIykoULF5bdnzt3LitWrChbPuC6665j+PDhZRO6rVYr9957L48++ig//PADmzZt4s4776Rz587079/f7q9T7CvAx4MRXcMALSMgIo6juMTGY3OTKCi2cXnbQG7v2cLsSPWeU5QlgE8++YTOnTszYMAABgwYQHR0NB999FG5bVJSUsjOzi67n56eTlxcHJGRkTz00EPExcXx2WefldvntddeY/jw4YwePZq+ffvi4+PD4sWLLzivSeqGsX0jAFi2PZMDJ/LMDSMiAvznlz1s2n+SBp5uvHhzdKVzaMU+nGKdJUendZac253/XcOvu47xpytaMfn6DmbHEZF6LCXjFEP//SuFJTZeHhnNqB76AlFtqlPrLInUprOjS3PW7ievsNjcMCJSbxWV2Hjki0QKS2xcGxnEyO5hZkeS36gsSb13dfsgwhv7kJNfzPyNh8yOIyL11P8t38W2wzlYvd2ZdlNnnX5zICpLUu+5uFi4+7dlBGat3IvNpjPTImJfWw9l8+aPuwD4541RBOkyTA5FZUkEGNUjDD9PN3YfzeWXXcfMjiMi9UhBcQmPfpFEsc1gcKcQhsWEmh1J/kBlSQRo4OVeNj9AywiIiD396/udpGSeorGvB88N76TTbw5IZUnkN2P6RGCxwIqUo+w+etrsOCJSD2zan8U7P+0G4PkRnWjspwWPHZHKkshvIgJ9uaZ96QWbZ69KMzeMiNR5+UUlPDo3CZsBw7uEMqhTU7MjyXmoLIn8zti+pdcNnLfhINlnikxOIyJ12ctLU9hzNJegBp48M6yT2XGkEipLIr/Tt01j2gX7kVdYwtz1ukCyiNSOtXtP8MFv8yNfvDkaq4+7yYmkMipLIr9jsVgY06d0dGnWqjRKtIyAiNSw3IJiHpubhGHA6B5hXB0ZZHYkuQCVJZE/GNG1GQE+7hzMOsP3yZlmxxGROmb6tzvYfyKPUKsXTw7paHYcqQKVJZE/8PZw5dbLSq/yrWUERKQmrdx1jI8S9gHw0sgY/L10+s0ZqCyJVOCu2HBcXSwk7DlBcnqO2XFEpA44lV/E4/M2A3Bn7xb0axtociKpKpUlkQqEBngzKCoE0OiSiNSM575O5tDJM7Ro5MOkwR3MjiMXQWVJ5DzG9o0A4MvEwxw/XWBuGBFxast3HOHz9QewWODlkdH4erqZHUkugsqSyHl0D29I52ZWCottfLZ2v9lxRMRJncwr5In5paff7unbkl6tGpucSC6WypLIeVgslrLRpY8S9lFUYjM3kIg4pamLtnHkVAGtmvgycWB7s+NINagsiVTihuimBPp5kplTwJIt6WbHEREnE781gy8TD+NigRmjYvBydzU7klSDypJIJTzdXLmzd+kyArN0vTgRuQjHTxfwj4VbALj/ytZ0bdHQ5ERSXSpLIhdwR69w3F0tbNp/ksQDJ82OIyJOwDAMpny1leO5hbQPbsDD/duaHUkugcqSyAU0aeDJ0OhQQMsIiEjVLN6czpItGbi5WJgxOgZPN51+c2YqSyJVMLZv6fXivtmcTmZOvslpRMSRHTmVz1NfbQXgr1e3oVMzq8mJ5FKpLIlUQecwKz3CG1JsM/j4t0sViIj8kWEYTF6whZN5RUSF+jP+mjZmR5IaoLIkUkVnR5c+XbOf/KISk9OIiCOav/EQ3ycfwcPVhRmjY3B31cdsXaB/iiJVNDAqmFCrF8dzC1mUdNjsOCLiYA6fPMMzi7cB8PB1bYkM8Tc5kdQUlSWRKnJzdSEuNgKAmSvTMAzD3EAi4jAMw+CJ+Zs5lV9Ml+YB/OnyVmZHkhqksiRyEW7r2RwvdxeS03NYs/eE2XFExEF8unY/v+w8hqdb6ek3N51+q1P0T1PkIgT4eDCiaxigZQREpNSBE3k8/00yABMHtqd1Ez+TE0lNU1kSuUhnrxe3bHsmB07kmRtGRExlsxk8NjeJvMISekY04p7fvggidYvKkshFahfcgH5tArEZ8OHqNLPjiIiJZq9OY83eE3i7u/LyqGhcXCxmR5JaoLIkUg1nR5fmrDtAbkGxuWFExBR7jp7mxfgdAEy+PpLwxr4mJ5LaorIkUg1Xtw8iorEPp/KLWbDxoNlxRMTOSn47/ZZfZKNvm8bc0Svc7EhSi1SWRKrBxcXC3X0iAJi5Kg2bTcsIiNQn//1lDxv3n8TP042XRsbo9Fsdp7IkUk0ju4fh5+nGnqO5/LzzqNlxRMROUjNPMeO7VACeGtKRZgHeJieS2qayJFJNDbzcGdXj7DICaeaGERG7KCqx8egXSRSW2Li6fZOy9wCp21SWRC7BmD4RWCzwU+pRdh05bXYcEallb6/YzZZD2Vi93Zl+czQWi06/1QcqSyKXILyxL9dGBgEwe1WauWFEpFZtO5zNGz/sBOCZYVEE+3uZnEjsRWVJ5BKN/W0RuvkbD5J9psjkNCJSGwqLS0+/FdsMBkYFc2OXULMjiR2pLIlcoj6tG9Mu2I+8whLmrj9gdhwRqQVv/LCTHRmnaOTrwfMjOuv0Wz2jsiRyiSwWC2P6lI4uzVqVRomWERCpU5IOnOTtn3YD8NzwTgT6eZqcSOxNZUmkBozo2owAH3cOZp3h++RMs+OISA3JLyrh0blJlNgMhsaEcn3npmZHEhOoLInUAG8PV269rAUAM1fuNTmNiNSUV5elsuvIaZo08OSfw6LMjiMmUVkSqSF3xYbj6mIhYc8JktNzzI4jIpdofdoJ3vtlDwDTb+pMQ18PkxOJWVSWRGpIaIA3g6JCAI0uiTi7vMJiHp2bhGGUrtZ/bYdgsyOJiVSWRGrQ2L4RAHyZeJjjpwvMDSMi1fbitzvYdzyPplYvnhra0ew4YjKVJZEa1D28IZ2bWSkstvHZ2v1mxxGRali16xizV+8D4MWbo/H3cjc5kZhNZUmkBlkslrLRpY8S9lFUYjM3kIhclFP5RUyctxmA23u14Ip2TUxOJI5AZUmkht0Q3ZRAP08ycwpYsiXd7DgichFeWJLMoZNnCGvozeTrO5gdRxyEypJIDfN0c+XO3meXEUgzN4yIVNmKlCN8trZ0Ff6XR8bg5+lmciJxFCpLIrXgjl7heLi6kHjgJJv2Z5kdR0QuIPtMEX+fvwWAMX0iiG3d2ORE4khUlkRqQZMGngyJKV3pV6NLIo7vmcXbyMjJp2WgL08MijQ7jjgYlSWRWnJP39LrxS3Zks43mzV3ScRRfbctgwUbD+FigVdGRePt4Wp2JHEwKksitaRTMyv9OwRRbDP466cb+eunGzmRW2h2LBH5nRO5hUxeWHr6bdwVrege3sjkROKIVJZEatFbd3TnoWva4Opi4ZvN6Qx47SeWbsswO5aI/GbKV1s5drqQtkF+TOjfzuw44qBUlkRqkYebC48MaM/Cv/ShbZAfx04Xcv9HG3h4ziZO5mmUScRMX28+zDeb03F1sfDq6C54uev0m1RMZUnEDqLDAvj6oX78+arWuFhKL4cy4LWf+SE50+xoIvXS0VMFTPlyKwB/vao1ncOsJicSR6ayJGInnm6uPDEoknl/7kOrJr4cOVXAvbPX89jcJLLPFJkdT6TeMAyDyQu3kJVXRMem/oy/pq3ZkcTBqSyJ2Fm3Fg1Z8tDljLu8JRYLzNtwkEGv/8xPqUfNjiZSLyzcdIhl2zNxd7UwY3QMHm76KJTK6S9ExARe7q7844aOzL0/lojGPqRn53P3B2uZtGAzpwuKzY4nUmdlZOfz9KJtAPzt2rZ0aOpvciJxBipLIibqEdGIJX+7nDF9IgD4bO0BBr72Myt3HTM3mEgdZBgGT8zfzKn8YmLCrDxwZWuzI4mTUFkSMZmPhxtTh0Xx2bjehDX05tDJM9zx3zVM+XIruRplEqkxn687wE+pR/Fwc2HG6BjcXPURKFWjvxQRBxHbujFLH76i7CK8HyXsY/C/fmHNnuMmJxNxfgdO5PHs19sBmDigPW2CGpicSJyJypKIA/H1dOO54Z35+N5eNAvwZv+JPG59L4FnFm/jTGGJ2fFEnJLNZvD4vM3kFpbQI7wh9/RraXYkcTJOU5aysrKIi4vDarVitVqJi4vj5MmTle6TmZnJmDFjCA0NxcfHh0GDBrFz586yn584cYIHH3yQ9u3b4+PjQ4sWLXjooYfIzs6u5VcjUrl+bQOJf/hybr2sOYZRejHe69/4hQ37TpgdTcTpfJSwj9V7juPt7soro2JwdbGYHUmcjNOUpdtvv53ExETi4+OJj48nMTGRuLi4825vGAbDhw9nz549fPXVV2zatInw8HD69+9Pbm4uAIcPH+bw4cO88sorbNmyhVmzZhEfH8+9995rr5clcl4NvNyZfnM0M8deRrC/J3uP5TLyndW8sCSZ/CKNMolUxYqUI0z/dgcAfx8cSUSgr8mJxBlZDMMwzA5xIcnJyXTs2JGEhAR69eoFQEJCArGxsezYsYP27dufs09qairt27dn69atREVFAVBSUkJQUBAvvvgi9913X4W/a+7cudx5553k5ubi5uZWpXw5OTlYrVays7Px99fXUKXmZecV8c+vtzN/40EAWjfxZcboLnRpHmBuMBEHlZyewwtLkvllZ+k3S/u0bszH9/bCRaNK8jtV/fx2ipGl1atXY7Vay4oSQO/evbFaraxatarCfQoKCgDw8vIqe8zV1RUPDw9+/fXX8/6uswessqJUUFBATk5OuZtIbbL6uDNjdAzv3dWDJg082X00l5veWsnLS3dQUKxRJpGzMnPyeXxeEte/8Qu/7DyGu6uFe/u15J247ipKUm1OUZYyMjIICgo65/GgoCAyMiq+gntkZCTh4eFMmjSJrKwsCgsLmT59OhkZGaSnp1e4z/Hjx3n22We5//77K80zbdq0srlTVquV5s2bX/yLEqmG6zoG893DV3Bjl1BsBvzf8t0M+/dKth7SPDup33ILinltWSpXvbyCL9YfxDDg+s4hfP/IlUwZ0hF/L3ezI4oTM7UsTZ06FYvFUult/fr1AFgs5/4XgWEYFT4O4O7uzvz580lNTaVRo0b4+PiwYsUKBg8ejKvruVeWzsnJ4YYbbqBjx448/fTTleaeNGkS2dnZZbcDBw5U49WLVE9DXw/+dWtX3rmzG419PUjJPMWN/7eSV5elUlhsMzueiF2V2Aw+X7efq19Zwb9+2MmZohK6tghg/p9jeeuO7oQ31hwluXRVm5RTS8aPH8+tt95a6TYRERFs3ryZzMxzr85+9OhRgoODz7tv9+7dSUxMJDs7m8LCQpo0aUKvXr3o0aNHue1OnTrFoEGD8PPzY+HChbi7V/5fIJ6ennh6ela6jUhtG9SpKZdFNGLKV1tZsiWDN37YyffbM5kxOkaXcJB64afUo7zwTTIpmacAaN7Im78P6sD1nUPO+x/SItXhVBO816xZQ8+ePQFYs2YNvXv3Pu8E74rs3LmTyMhIvv32WwYMGACUjigNHDgQT09PlixZgo+Pz0Xn0wRvMdvipMNM+WorJ/OKcHe18Ldr2/LAla21QrHUSTsycnj+m/9N3rZ6u/PgNW2Iiw3H0+3cMwci51PVz2+nKEsAgwcP5vDhw7z77rsA/OlPfyI8PJzFixeXbRMZGcm0adMYMWIEUPrNtiZNmtCiRQu2bNnC3/72N7p37878+fOB0hGl6667jry8PBYuXIiv7/+Ga5s0aVLh6bqKqCyJIzhyKp9/LNzKsu2lo7DRYVZeGRVDu2CtVCx1Q2ZOPq9+l8rcDQewGeDuauGu2AgevKYNAT4eZscTJ1TVz29TT8NdjE8++YSHHnqobERo2LBhvPnmm+W2SUlJKbegZHp6Oo888giZmZk0bdqUu+66iylTppT9fMOGDaxZswaANm3alHuuvXv3EhERUUuvRqTmBTXw4j9x3fky8RBPf7WNzQezGfLGr0y4rh1/uqKVFuITp5VbUMx/ft7Df37ew5nf1hi7vnMITwyK1JwksQunGVlyZBpZEkeTmZPP3+dvZnnKUQC6tgjglVExtG7iZ3IykaorsRnM23CAGd+lcuRU6XIwXVsE8OQNHege3sjkdFIX1LnTcI5MZUkckWEYzN1wkGcXb+dUQTGebi5MHNiesX1bapRJHJ4mb4s9qCzZkcqSOLLDJ8/wxPzNZZNhL4toyMsjY3TZB3FIOzJyeGHJDn5OLR0V1eRtqU0qS3aksiSOzjAM5qw7wHNfbye3sAQvdxf+PiiSu2IjtKqxOARN3hYzqCzZkcqSOIsDJ/J4fN5mVu85DkDvVo14eWQMzRtd/JIZIjVBk7fFTCpLdqSyJM7EZjP4eM0+pi3ZwZmiEnw9XJl8Qwdu79lCc0HEbjR5WxyBypIdqSyJM9p3PJeJczezNu0EAJe3DWT6zdE0C/A2OZnUdT+lHmXakmR2ZGjytphLZcmOVJbEWdlsBjNXpfFS/A4Kim008HRjypCOjOoRpg8tqXGavC2ORmXJjlSWxNntOXqax+YmsXH/SQCubt+EaTdFE2L1MjeY1AmavC2OSmXJjlSWpC4osRn895c9zFiWSmGxDX8vN6YOi2JE12YaZZJqySss5t2fNHlbHJfKkh2pLEldsjPzFI/NTSLpYOmlg/p3COaFmzoR1ECjTFI1mrwtzkJlyY5UlqSuKS6x8e7Pe3j9+1SKSgwCfNz5542dGBrdVKNMUilN3hZnorJkRypLUlftyMjh0S+S2HY4B4DBnUJ4bngnGvt5mpxMHM0fJ2/7e7nx0LVtNXlbHJrKkh2pLEldVlRi463lu/n3jzspthk09vXgueGdGNy5qdnRxAFo8rY4M5UlO1JZkvpg66FsHpubVHZ6ZVhMKM8Mi6Khrz4Q66O8wtKVt9/9SZO3xXmpLNmRypLUF4XFNt74YSdv/7SbEptBoJ8n027qzHUdg82OJnaiydtSl6gs2ZHKktQ3SQdO8ujcJHYdOQ3ATV2b8fTQKKw+7iYnk9qkydtS16gs2ZHKktRH+UUlvPZ9Ku/9vAebAcH+nky/OZqr2weZHU1qmCZvS12lsmRHKktSn23Yl8XEuUnsOZYLwC09mvOPIR3w99Iok7M7kpPPDE3eljpMZcmOVJakvssvKuHlpSl8sHIvhgGhVi9eHBnN5W2bmB1NqkGTt6W+UFmyI5UlkVJr955g4rwk9h3PA+D2Xi2YfH0H/DzdTE4mVaHJ21LfqCzZkcqSyP/kFRbz4rc7mL16HwBhDb15aWQ0fVoHmpxMKvNz6lFe0ORtqWdUluxIZUnkXKt2H+PxeZs5mHUGgDF9Inh8UHt8PDTK5Eg0eVvqM5UlO1JZEqnY6YJiXliSzKdr9gMQ3tiHV0bFcFmETumYTZO3RVSW7EplSaRyP6ce5e/zN3M4Ox+LBe7p25KJA9vj5a6RC3vT5G2R/1FZsiOVJZELy8kv4rmvt/PF+oMAtAr05ZXRMXRr0dDkZPWDJm+LnEtlyY5UlkSqbvmOI/x9wWYycwpwscCfrmjNw/3bapSpFmnytkjFVJbsSGVJ5OJk5xXxzOJtLNh0CIC2QX7MGB1DdFiAucHqGE3eFqmcypIdqSyJVM932zKYvHArx04X4Opi4S9XtebBa9ri4eZidjSnpsnbIlWjsmRHKksi1ZeVW8hTi7axOOkwAJEhDZgxOoaoUKvJyZyPJm+LXByVJTtSWRK5dEu2pPPkl1s5kVuIm4uFB69py1+ubo27q0aZLqTEZjB/w0Fe+S5Fk7dFLoLKkh2pLInUjGOnC5jy5Va+3ZoBQFSoPzNGxxAZon+vzqeiydtPDIrkhs5NNXlb5AJUluxIZUmk5hiGweLN6Tz11VZO5hXh7mrh4f7tuP+KVrhplKmMJm+LXDqVJTtSWRKpeUdO5TN5wVa+T84EIKZ5ADNGRdMmqIHJycx1JCefV5el8sV6Td4WuVQqS3aksiRSOwzDYOGmQzy9aBun8ovxcHPhsQHtuLdfK1xd6tcpprOTt//z8x7yCjV5W6QmqCzZkcqSSO3KyM7n7ws2syKl9JRTtxYBvDIqhlZN/ExOVvs0eVuk9qgs2ZHKkkjtMwyDuesP8s+vt3O6oBgvdxceHxjJmD4RuNTRUSZN3hapXSpLdqSyJGI/h06e4Yl5m/l11zEAerZsxCsjY2jR2MfkZDUnJeMUzy9J1uRtkVqmsmRHKksi9mUYBp+u3c/z3ySTV1iCj4crkwZHckevcKceZdLkbRH7UlmyI5UlEXMcOJHHxHlJJOw5AUCf1o158eZomjdyrlGm803efnxgJBGBmrwtUltUluxIZUnEPDabwUcJ+5j+7Q7OFJXg6+HKk0M6cutlzR1+Xo8mb4uYS2XJjlSWRMyXdiyXx+YmsX5fFgBXtGvCizd3pqnV2+RkFdPkbRHzqSzZkcqSiGMosRnMXLmXl5emUFBso4GXG08N6cjI7mEOU0BSMk7xwpJkftLkbRHTqSzZkcqSiGPZffQ0j81NYtP+kwBcGxnECzd1Jtjfy7RMmrwt4nhUluxIZUnE8ZTYDN77ZQ+vfpdKYYkNq7c7zwyL4sYuoXYdZdLkbRHHpbJkRypLIo4rNfMUj36RxJZD2QAMjArmueGdadLAs1Z/ryZvizg+lSU7UlkScWxFJTbe/Wk3//phJ0UlBg193Hl2eCeGRIfWyu/7ZedRnv9Gk7dFHJ3Kkh2pLIk4h+T0HB79Iont6TkA3NC5Kc8O70Qj35qZM6TJ2yLORWXJjlSWRJxHYbGN/1u+i/9bvotim0GgnwfPDe/MoE4h1X7OiiZvx/WO4KFrNXlbxJGpLNmRypKI89l6KJtHv0giJbP0VNnwLqFMHRZ1UeVGk7dFnJvKkh2pLIk4p4LiEt74YSdvr9iNzYAmDTyZflNnru0QXOl+mrwtUjeoLNmRypKIc0s8cJJHv0hk99FcAG7uFsZTQzti9XY/Z1tN3hapO1SW7EhlScT55ReV8OqyVN77ZQ+GASH+Xky/uTNXtQ8CNHlbpC5SWbIjlSWRumPDvhM8Nncze4+VjjLdellzAE3eFqmDVJbsSGVJpG45U1jCy0tTmLlqL79/h9TkbZG6paqf3252zCQi4hS8PVx5amhHBkYF89RX27B6u/P4oPb0iNDkbZH6SCNLNUAjSyIiIs6nqp/fLnbMJCIiIuJ0VJZEREREKqGyJCIiIlIJlSURERGRSqgsiYiIiFRCZUlERESkEk5TlrKysoiLi8NqtWK1WomLi+PkyZOV7pOZmcmYMWMIDQ3Fx8eHQYMGsXPnzgq3NQyDwYMHY7FY+PLLL2v+BYiIiIhTcpqydPvtt5OYmEh8fDzx8fEkJiYSFxd33u0Nw2D48OHs2bOHr776ik2bNhEeHk7//v3Jzc09Z/vXX39dF8EUERGRczjFCt7JycnEx8eTkJBAr169AHjvvfeIjY0lJSWF9u3bn7PPzp07SUhIYOvWrURFRQHw1ltvERQUxGeffcZ9991Xtm1SUhKvvvoq69ato2nTpvZ5USIiIuIUnGJkafXq1Vit1rKiBNC7d2+sViurVq2qcJ+CggIAvLy8yh5zdXXFw8ODX3/9teyxvLw8brvtNt58801CQkJq6RWIiIiIs3KKspSRkUFQUNA5jwcFBZGRkVHhPpGRkYSHhzNp0iSysrIoLCxk+vTpZGRkkJ6eXrbdhAkT6NOnDzfeeGOV8xQUFJCTk1PuJiIiInWTqWVp6tSpWCyWSm/r168HqHA+kWEY551n5O7uzvz580lNTaVRo0b4+PiwYsUKBg8ejKurKwCLFi3ixx9/5PXXX7+o3NOmTSubaG61WmnevPnFvXARERFxGqbOWRo/fjy33nprpdtERESwefNmMjMzz/nZ0aNHCQ4OPu++3bt3JzExkezsbAoLC2nSpAm9evWiR48eAPz444/s3r2bgICAcvvdfPPNXH755axYsaLC5500aRKPPPJI2f2cnBwVJhERkTrKYhiGYXaIC0lOTqZjx46sWbOGnj17ArBmzRp69+7Njh07KpzgXZGdO3cSGRnJt99+y4ABA8jIyODYsWPltuncuTP/+te/GDp0KC1btqzS81b1qsUiIiLiOKr6+e0U34br0KEDgwYNYty4cbz77rsA/OlPf2LIkCHlilJkZCTTpk1jxIgRAMydO5cmTZrQokULtmzZwt/+9jeGDx/OgAEDAAgJCalwUneLFi2qXJSg9HQgoLlLIiIiTuTs5/aFxo2coiwBfPLJJzz00ENlRWfYsGG8+eab5bZJSUkhOzu77H56ejqPPPIImZmZNG3alLvuuospU6bUeLZTp04B6FSciIiIEzp16hRWq/W8P3eK03COzmazcfjwYRo0aOAwC1uenUd14MABnRpEx+OPdDzK0/EoT8ejPB2Pc9WVY2IYBqdOnSI0NBQXl/N/581pRpYcmYuLC2FhYWbHqJC/v79T/yHXNB2P8nQ8ytPxKE/Hozwdj3PVhWNS2YjSWU6xzpKIiIiIWVSWRERERCqhslRHeXp68vTTT+Pp6Wl2FIeg41Gejkd5Oh7l6XiUp+Nxrvp2TDTBW0RERKQSGlkSERERqYTKkoiIiEglVJZEREREKqGy5OQOHTrEnXfeSePGjfHx8aFLly5s2LCh7OeGYTB16lRCQ0Px9vbmqquuYtu2bSYmrl2VHY+ioiKeeOIJOnfujK+vL6Ghodx1110cPnzY5NS150J/H793//33Y7FYeP311+0b0o6qcjySk5MZNmwYVquVBg0a0Lt3b/bv329S4tp1oeNx+vRpxo8fT1hYGN7e3nTo0IG3337bxMS1KyIiAovFcs7tr3/9K1D/3k8rOx717f1UZcmJZWVl0bdvX9zd3fn222/Zvn07M2bMICAgoGybl156iVdffZU333yTdevWERISwnXXXVd2iZa65ELHIy8vj40bNzJlyhQ2btzIggULSE1NZdiwYeYGryVV+fs468svv2TNmjWEhobaP6idVOV47N69m379+hEZGcmKFStISkpiypQpeHl5mRe8llTleEyYMIH4+Hg+/vhjkpOTmTBhAg8++CBfffWVecFr0bp160hPTy+7LVu2DIBRo0YB9ev9FCo/HvXt/RRDnNYTTzxh9OvX77w/t9lsRkhIiDF9+vSyx/Lz8w2r1Wq888479ohoVxc6HhVZu3atARj79u2rpVTmqerxOHjwoNGsWTNj69atRnh4uPHaa6/VfjgTVOV43HLLLcadd95pp0TmqsrxiIqKMv75z3+We6xbt27Gk08+WZvRHMbf/vY3o3Xr1obNZqt376cV+f3xqEhdfj/VyJITW7RoET169GDUqFEEBQXRtWtX3nvvvbKf7927l4yMjLKLD0Pp2hhXXnklq1atMiNyrbrQ8ahIdnY2FoulwtEWZ1eV42Gz2YiLi2PixIlERUWZlNQ+LnQ8bDYb33zzDe3atWPgwIEEBQXRq1cvvvzyS/NC16Kq/H3069ePRYsWcejQIQzDYPny5aSmpjJw4ECTUttPYWEhH3/8Mffccw8Wi6XevZ/+0R+PR0Xq8vupRpacmKenp+Hp6WlMmjTJ2Lhxo/HOO+8YXl5exuzZsw3DMIyVK1cagHHo0KFy+40bN84YMGCAGZFr1YWOxx+dOXPG6N69u3HHHXfYOal9VOV4vPDCC8Z1111X9l+KdXlk6ULHIz093QAMHx8f49VXXzU2bdpkTJs2zbBYLMaKFStMTl/zqvL3UVBQYNx1110GYLi5uRkeHh7Ghx9+aGJq+/n8888NV1fXsvfP+vZ++kd/PB5/VNffT1WWnJi7u7sRGxtb7rEHH3zQ6N27t2EY//uX+/Dhw+W2ue+++4yBAwfaLae9XOh4/F5hYaFx4403Gl27djWys7PtFdGuLnQ81q9fbwQHB5d786vLZelCx+PQoUMGYNx2223lthk6dKhx66232i2nvVTl35eXX37ZaNeunbFo0SIjKSnJ+Pe//234+fkZy5Yts3dcuxswYIAxZMiQsvv17f30j/54PH6vPryf6jScE2vatCkdO3Ys91iHDh3KvrkTEhICQEZGRrltjhw5QnBwsH1C2tGFjsdZRUVFjB49mr1797Js2TKnv2L2+VzoePzyyy8cOXKEFi1a4ObmhpubG/v27ePRRx8lIiLChMS160LHIzAwEDc3tyr9DdUFFzoeZ86cYfLkybz66qsMHTqU6Ohoxo8fzy233MIrr7xiRmS72bdvH99//z333Xdf2WP17f309yo6HmfVl/dTlSUn1rdvX1JSUso9lpqaSnh4OAAtW7YkJCSk7BsMUHre+aeffqJPnz52zWoPFzoe8L9/sXfu3Mn3339P48aN7R3Tbi50POLi4ti8eTOJiYllt9DQUCZOnMjSpUvNiFyrLnQ8PDw8uOyyyy74N1RXXOh4FBUVUVRUhItL+Y8JV1dXbDab3XKaYebMmQQFBXHDDTeUPVbf3k9/r6LjAfXr/VSn4ZzY2rVrDTc3N+P55583du7caXzyySeGj4+P8fHHH5dtM336dMNqtRoLFiwwtmzZYtx2221G06ZNjZycHBOT144LHY+ioiJj2LBhRlhYmJGYmGikp6eX3QoKCkxOX/Oq8vfxR3X5NFxVjseCBQsMd3d34z//+Y+xc+dO49///rfh6upq/PLLLyYmrx1VOR5XXnmlERUVZSxfvtzYs2ePMXPmTMPLy8t46623TExeu0pKSowWLVoYTzzxxDk/q0/vp2ed73jUt/dTlSUnt3jxYqNTp06Gp6enERkZafznP/8p93ObzWY8/fTTRkhIiOHp6WlcccUVxpYtW0xKW/sqOx579+41gApvy5cvNy90LbrQ38cf1eWyZBhVOx7vv/++0aZNG8PLy8uIiYkxvvzySxOS2seFjkd6eroxZswYIzQ01PDy8jLat29vzJgx47xfHa8Lli5dagBGSkrKOT+rb++nhnH+41Hf3k8thmEYZoxoiYiIiDgDzVkSERERqYTKkoiIiEglVJZEREREKqGyJCIiIlIJlSURERGRSqgsiYiIiFRCZUlERESkEipLIiIiIpVQWRKRei0iIoLXX3/d7Bgi4sBUlkTEaQ0dOpT+/ftX+LPVq1djsVjYuHGjnVOJSF2jsiQiTuvee+/lxx9/ZN++fef87IMPPqBLly5069bNhGQiUpeoLImI0xoyZAhBQUHMmjWr3ON5eXl8/vnn3HvvvcyfP5+oqCg8PT2JiIhgxowZ532+tLQ0LBYLiYmJZY+dPHkSi8XCihUrAFixYgUWi4WlS5fStWtXvL29ueaaazhy5AjffvstHTp0wN/fn9tuu428vLyy5zEMg5deeolWrVrh7e1NTEwM8+bNq8nDISK1RGVJRJyWm5sbd911F7NmzeL31wSfO3cuhYWFxMbGMnr0aG699Va2bNnC1KlTmTJlyjnlqjqmTp3Km2++yapVqzhw4ACjR4/m9ddf59NPP+Wbb75h2bJl/Pvf/y7b/sknn2TmzJm8/fbbbNu2jQkTJnDnnXfy008/XXIWEaldFuP37zAiIk5mx44ddOjQgR9//JGrr74agCuvvJJmzZphsVg4evQo3333Xdn2jz/+ON988w3btm0DSid4P/zwwzz88MOkpaXRsmVLNm3aRJcuXYDSkaWGDRuyfPlyrrrqKlasWMHVV1/N999/z7XXXgvA9OnTmTRpErt376ZVq1YAPPDAA6SlpREfH09ubi6BgYH8+OOPxMbGlmW57777yMvL49NPP7XHoRKRatLIkog4tcjISPr06cMHH3wAwO7du/nll1+45557SE5Opm/fvuW279u3Lzt37qSkpOSSfm90dHTZ/w8ODsbHx6esKJ197MiRIwBs376d/Px8rrvuOvz8/MpuH374Ibt3776kHCJS+9zMDiAicqnuvfdexo8fz//93/8xc+ZMwsPDufbaazEMA4vFUm7bygbTXVxcztmmqKiowm3d3d3L/r/FYil3/+xjNpsNoOx/v/nmG5o1a1ZuO09Pzwu9PBExmUaWRMTpjR49GldXVz799FNmz57N2LFjsVgsdOzYkV9//bXctqtWraJdu3a4urqe8zxNmjQBID09veyx30/2rq6OHTvi6enJ/v37adOmTblb8+bNL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diff --git a/parsers/README.md b/parsers/README.md deleted file mode 100644 index db7e096..0000000 --- a/parsers/README.md +++ /dev/null @@ -1,22 +0,0 @@ -# Parsers - -This folder is meant for any code related to the effort of sharing parsers between workflow engines. - -## Known parsers - -Here we keep track of a list of known parser tools for various codes in the materials science community. -These can be either _input_ or _output_ parser tools. - -### Quantum ESPRESSO - -* ASE: https://gitlab.com/ase/ase/-/blob/master/ase/io/espresso.py?ref_type=heads -* https://github.com/aiidateam/qe-tools - -### VASP - -* ASE: https://gitlab.com/ase/ase/-/blob/master/ase/io/vasp.py?ref_type=heads -* https://github.com/aiida-vasp/parsevasp - -### Yambo - -* https://github.com/yambo-code/yambopy diff --git a/parsers/adis-tools/pyproject.toml b/parsers/adis-tools/pyproject.toml deleted file mode 100644 index f223876..0000000 --- a/parsers/adis-tools/pyproject.toml +++ /dev/null @@ -1,17 +0,0 @@ -[build-system] -requires = ["flit_core >=3.4,<4"] -build-backend = "flit_core.buildapi" - -[project] -name = "adis-tools" -version = "0.1.0" -description = "Tools for the ADIS workflow project." -dependencies = [ - "qe_tools~=2.0", - "ase", - "numpy", - "xmlschema~=2.0" -] - -[tool.flit.module] -name = "adis_tools" diff --git a/parsers/relax.xml b/parsers/relax.xml deleted file mode 100644 index e7c91a6..0000000 --- a/parsers/relax.xml +++ /dev/null @@ -1,12492 +0,0 @@ - - - - - QEXSD_23.03.10 - XML file generated by PWSCF - This run was terminated on: 15:40:40 30 Nov 2023 - - - - 1 - 1 - 1 - 1 - 1 - 1 - - - - - relax - from_scratch - aiida - ./pseudo/ - ./out/ - true - true - true - low - 3420 - 50 - 1.000000000000000E-005 - 5.000000000000000E-005 - 5.000000000000000E-001 - high - 100000 - false - false - - - - 2.808550000000000E+001 - Si.pbesol-n-rrkjus_psl.1.0.0.UPF - - - - - 6.328500573532568E+000 4.474925670285654E+000 1.096128452905589E+001 - 4.219000382292054E+000 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1.992726325988770E-002 - - - 2.448169999999976E-001 - 3.702023029327393E-001 - - - 7.149999999853662E-004 - 8.611679077148438E-004 - - - 1.147200000000481E-002 - 1.293754577636719E-002 - - - 6.084999999991680E-003 - 6.389617919921875E-003 - - - 2.471000000014101E-003 - 2.578020095825195E-003 - - - 4.879555000000003E+000 - 5.037681818008423E+000 - - - 4.614800000000017E-001 - 4.868972301483154E-001 - - - 4.420439999999957E-001 - 4.657781124114990E-001 - - - 1.221000000001027E-003 - 1.269102096557617E-003 - - - 1.580699999999524E-002 - 1.735687255859375E-002 - - - 1.484999999995296E-003 - 1.485109329223633E-003 - - - 4.417881000000001E+000 - 4.475630044937134E+000 - - - - diff --git a/parsers/sandbox.ipynb b/parsers/sandbox.ipynb deleted file mode 100644 index 78e8665..0000000 --- a/parsers/sandbox.ipynb +++ /dev/null @@ -1,101 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from adis_tools.parsers import parse_pw" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'ase_structure': Atoms(symbols='Si2', pbc=True, cell=[[3.348898268062178, 0.0, 1.9334873165488495], [1.1162994227207257, 3.1573715664993287, 1.9334873165488495], [0.0, 0.0, 3.866974633097699]]),\n", - " 'energy': -308.54069951701086,\n", - " 'forces': [[6.049357433713179e-06, -6.611385511736578e-06],\n", - " [0.0, -6.049357433713179e-06],\n", - " [6.611385511736578e-06, 0.0]]}" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "parse_pw('relax.xml')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "\n", - "with Path('relax.xml').open('r') as f:\n", - " res = parse_pw(f)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'ase_structure': Atoms(symbols='Si2', pbc=True, cell=[[3.348898268062178, 0.0, 1.9334873165488495], [1.1162994227207257, 3.1573715664993287, 1.9334873165488495], [0.0, 0.0, 3.866974633097699]]),\n", - " 'energy': -308.54069951701086,\n", - " 'forces': array([[ 6.04935743e-06, -6.61138551e-06],\n", - " [ 0.00000000e+00, -6.04935743e-06],\n", - " [ 6.61138551e-06, 0.00000000e+00]])}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "adis", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/postBuild b/postBuild new file mode 100644 index 0000000..5aeee72 --- /dev/null +++ b/postBuild @@ -0,0 +1 @@ +pip install -e aiida_qe_basic diff --git a/pyiron_base.ipynb b/pyiron_base.ipynb new file mode 100644 index 0000000..084b25a --- /dev/null +++ b/pyiron_base.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.11.8","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os","metadata":{"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np","metadata":{"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"from ase.build import bulk\nfrom ase.io import write","metadata":{"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"from adis_tools.parsers import parse_pw","metadata":{"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"# Functions","metadata":{}},{"cell_type":"code","source":"def generate_structures(structure, strain_lst): \n structure_lst = []\n for strain in strain_lst:\n structure_strain = structure.copy()\n structure_strain.set_cell(\n structure_strain.cell * strain**(1/3), \n scale_atoms=True\n )\n structure_lst.append(structure_strain)\n return structure_lst","metadata":{"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"def plot_energy_volume_curve(volume_lst, energy_lst):\n plt.plot(volume_lst, energy_lst)\n plt.xlabel(\"Volume\")\n plt.ylabel(\"Energy\")\n plt.savefig(\"evcurve.png\")","metadata":{"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"def write_input(input_dict, working_directory=\".\"):\n filename = os.path.join(working_directory, 'input.pwi')\n os.makedirs(working_directory, exist_ok=True)\n write(\n filename=filename, \n images=input_dict[\"structure\"], \n Crystal=True, \n kpts=input_dict[\"kpts\"], \n input_data={\n 'calculation': input_dict[\"calculation\"],\n 'occupations': 'smearing',\n 'degauss': input_dict[\"smearing\"],\n }, \n pseudopotentials=input_dict[\"pseudopotentials\"],\n tstress=True, \n tprnfor=True\n )","metadata":{"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"def collect_output(working_directory=\".\"):\n output = parse_pw(os.path.join(working_directory, 'pwscf.xml'))\n return {\n \"structure\": output['ase_structure'],\n \"energy\": output[\"energy\"],\n \"volume\": output['ase_structure'].get_volume(),\n }","metadata":{"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"def workflow(project, structure, pseudopotentials): \n # Structure optimization \n job_qe_minimize = pr.wrap_executable(\n job_name=\"job_qe_minimize\",\n write_input_funct=write_input,\n collect_output_funct=collect_output,\n input_dict={\n \"structure\": structure, \n \"pseudopotentials\": pseudopotentials, \n \"kpts\": (3, 3, 3),\n \"calculation\": \"vc-relax\",\n \"smearing\": 0.02,\n },\n executable_str=\"mpirun -np 1 pw.x -in input.pwi > output.pwo\",\n execute_job=True,\n )\n\n # Generate Structures\n structure_lst = pr.wrap_python_function(generate_structures)(\n structure=job_qe_minimize.output.structure, \n strain_lst=np.linspace(0.9, 1.1, 5),\n )\n \n # Energy Volume Curve \n energy_lst, volume_lst = [], []\n for i, structure_strain in enumerate(structure_lst):\n job_strain = pr.wrap_executable(\n job_name=\"job_strain_\" + str(i),\n write_input_funct=write_input,\n collect_output_funct=collect_output,\n input_dict={\n \"structure\": structure_strain, \n \"pseudopotentials\": pseudopotentials, \n \"kpts\": (3, 3, 3),\n \"calculation\": \"scf\",\n \"smearing\": 0.02,\n },\n executable_str=\"mpirun -np 1 pw.x -in input.pwi > output.pwo\",\n execute_job=True,\n )\n energy_lst.append(job_strain.output.energy)\n volume_lst.append(job_strain.output.volume)\n \n return {\"volume\": volume_lst, \"energy\": energy_lst}","metadata":{"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":"# Setup","metadata":{}},{"cell_type":"code","source":"from pyiron_base import Project","metadata":{"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"pr = Project(\"test\")\npr.remove_jobs(recursive=True, silently=True)","metadata":{"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"0it [00:00, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ad61406143494ae5a2d4a01b0cb3750d"}},"metadata":{}}]},{"cell_type":"markdown","source":"# Workflow","metadata":{}},{"cell_type":"code","source":"job_workflow = pr.wrap_python_function(workflow)\njob_workflow.input.project = pr\njob_workflow.input.structure = bulk('Al', a=4.05, cubic=True)\njob_workflow.input.pseudopotentials = {\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"}\njob_workflow.run()","metadata":{"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"The job workflowdbcdde11bde789bfe23b268a60c426c2 was saved and received the ID: 1\nThe job job_qe_minimize was saved and received the ID: 2\nThe job generate_structuresffd5c51457c48a1cff1923a2d98eb48c was saved and received the ID: 3\nThe job job_strain_0 was saved and received the ID: 4\nThe job job_strain_1 was saved and received the ID: 5\nThe job job_strain_2 was saved and received the ID: 6\nThe job job_strain_3 was saved and received the ID: 7\nThe job job_strain_4 was saved and received the ID: 8\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Result","metadata":{}},{"cell_type":"code","source":"plot_energy_volume_curve(\n volume_lst=job_workflow.output.result[\"volume\"], \n energy_lst=job_workflow.output.result[\"energy\"]\n)","metadata":{"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} diff --git a/semantics.md b/semantics.md deleted file mode 100644 index e42667d..0000000 --- a/semantics.md +++ /dev/null @@ -1,166 +0,0 @@ -# Semantics - -## Common language and definitionns - -### Parsers - -Wikipedia definition: https://en.wikipedia.org/wiki/Parsing#Parser - -A parser is a tool that takes input data and converts the contents into **serialized** or **serializable** data using an established format or schema. -Immediately, we can consider two types of input data: - -* Text: the `stdout` of VASP, an XML file, ... -* Software/language-specific data structures: a float, a Python `dict`, FORTRAN array, ... - -One can develop parsers for both the inputs and outputs of a software package or code. -Still, some distinctions between the two can be made: - -#### Output parser - -* Typically does not have to be complete. The `stdout` of a code often will contain information that does not require parsing. - Moreover, we might not be interested in _all_ of the output provided (e.g. the difference in integrated charge density for each SCF step.) -* Typically shouldn't be able to _generate_ the data that we want to parse. - -#### Input parser - -* Typically _does_ have to be complete, or _as complete_ as the parser of the software we are running. - If we want to understand how a certain calculation was run, we need to know all of the inputs. - Exceptions here are comments that are not processed by the software. -* Typically _should_ be able to generate the data that we want to parse. - That is, if we consider this to be part of the functions of a parser. - - - `serialized data`: JSON, XML, yml etc. - - `serializable data`: python dictionary (entries must be also serializable), python list, numbers etc. - -## Software-specific semantics -### AiiDA - -- `Work chain`: A work chain is one of the two types of workflows that AiiDA (currently) offers. Its defining feature (in contrast with work _functions_) is that it allows for checkpoints. The work chain is defined as a `class`: - - ```python - class EquationOfState(engine.WorkChain): - """WorkChain to compute Equation of State using Quantum ESPRESSO.""" - - @classmethod - def define(cls, spec): - """Specify inputs and outputs.""" - super().define(spec) - spec.input("code", valid_type=orm.Code) - spec.input("structure", valid_type=orm.StructureData) - spec.input("scale_factors", valid_type=orm.List) - - spec.outline( - cls.run_eos, - cls.results, - ) - spec.output("eos_dict", valid_type=orm.Dict) - ``` - - Let's look at the various components: - - * All work chains start with the `define` method and calling the corresponding method of its parent class. - - ```python - @classmethod - def define(cls, spec): - """Specify inputs and outputs.""" - super().define(spec) - ``` - - * All inputs and outputs are defined as [ports](https://aiida.readthedocs.io/projects/aiida-core/en/latest/topics/processes/usage.html?highlight=port%20namespace#ports-and-port-namespaces): - - ```python - spec.input("code", valid_type=orm.Code) - spec.input("structure", valid_type=orm.StructureData) - spec.input("scale_factors", valid_type=orm.List) - - ... - - spec.output("eos_dict", valid_type=orm.Dict) - ``` - - * The logic of the workflow is specified in the _outline_: - - ```python - spec.outline( - cls.run_eos, - cls.results, - ) - ``` - - and is defined using methods specified on the class. These methods are then executed according to the logic of the outline. In between the execution, the work chain is checkpointed; i.e. the daemon will wait until other processes in the "context" are finished. The usual use case is that you want to run a list of e.g. Quantum ESPRESSO calculations in the `run_eos` step before executing the next step. - - More information in the documentation: https://aiida.readthedocs.io/projects/aiida-core/en/latest/topics/workflows/concepts.html#work-chains - -- [`Builder`](https://aiida.readthedocs.io/projects/aiida-core/en/latest/topics/processes/usage.html?highlight=builder#process-builder): The process builder is a tool that helps you "build" the inputs of a calculation job or work chain. Say you want to run the EoS work chain. One way to run the work chain would be: - - ```python - engine.run(EquationOfState, code=orm.load_code('qe-7.2-pw@localhost'), - structure=si_structure, - scale_factors=orm.List([0.9, 0.95, 1.0, 1.05, 1.1])) - ``` - - However, this means you would need to _know_ what the inputs of the process are, their types, etc. The builder makes this easier, since you can tab-complete the inputs. Moreover, you can get information on each of the inputs: - - ```python - builder.code? - - Type: property - String form: - Docstring: - "name": "code", - "required": "True" - "non_db": "False" - "valid_type": "" - "help": "The Code to use for this job.", - - builder.structure? - ``` - -- `profile`: A profile is similar to a "project" in a sense. Each profile defines: - - * The user: this is stored in a separate table in the database, and every node is linked to a user. - * The storage, which consists of two components: - * PostgreSQL database - * Repository (i.e. files), stored as a [disk objectstore](https://pypi.org/project/disk_objectstore/). - - Typically you would only need one profile and never look back. However, I find it useful to have a `dev` profile for testing things and then a `prod` profile for the actual runs I want to keep. - - Another note here is that since profiles share the same AiiDA install, they have the same Python environment. What _I_ typically call an [AiiDA project](https://github.com/aiidateam/aiida-project) is a Python environment with a corresponding structured directory. This is because for different projects, I typically would install different plugin packages, so I don't want to use one environment for all my projects. Especially when I'm developing in one project, I don't want this to mess up the runs that are active in other one. So each project will have its own environment, and in one project I will have multiple profiles in case I want to separate the data. - -- port: Ports are basically the definition of the in- and outputs of a process. Compared to e.g. the inputs of a function, the process ports allow you to specify more details on the input: - - * Validation: You can specify the "data type" of the input nodes (with the `valid_type` argument), but also do more validation by specifying a `validator` to check not only the types but also the value. - * Documentation: By adding the `help` argument, you can explain the input/output. This can be used to automatically generate documentation, see e.g. [here](https://aiida-quantumespresso.readthedocs.io/en/latest/topics/calculations/pw.html). - * Specify if an input is metadata or not. Metadata is stored in the database (as an attribute on the node), but is not a node in itself. An example is the Slurm account you want to run the calculation with. - - Besides simple ports, you also can specify _port namespaces_. These allow you to define multiple ports in one port namespace (think of it like a dictionary). - - - More links: - - * https://aiida.readthedocs.io/projects/aiida-core/en/latest/topics/processes/usage.html?highlight=port%20namespace#ports-and-port-namespaces - * https://plumpy.readthedocs.io/en/latest/tutorial.html#defining-inputs-and-outputs - -- `context`: The context of a work chain is in essence a dictionary that can be used to pass information between the steps (defined as separate methods) of the work chain. - -- `daemon`: This is explained quite well and succinctly in the documentation: https://aiida.readthedocs.io/projects/aiida-core/en/latest/topics/daemon.html - -- `attributes/extras`: Every node stores data in the database in two ways: - - * attributes: once a node is stored, the attributes become immutable, so can never be changed from this point. - * extras: this is data you can still add to/change on a node even after it is stored. - - - Also relevant: https://aiida.readthedocs.io/projects/aiida-core/en/latest/topics/processes/concepts.html?highlight=attributes#process-sealing - - -### Automate - -- `Job`: A class that allows to delay the execution of a function. The outputs of jobs are stored in databases. -- `Flow`: Class that contains Jobs and other Flows. Jobs and Flows are typically connected via the outputs of individual jobs that are inputs of other jobs (A Flow can be represented as a directed, acyclic graph with the jobs as nodes) -- `Maker`: Class (typically a dataclass) to generate a Job or Flow in a convenient way -- `Response`: Class to replace a job with another job or flow during the run time (also additions and detours are possible) (Use case: structure optimization changes a structure so drastically that the space group changes, i.e. it is not known how many displacement runs one would need to perform in a harmonic phonon run before the optimization has taken place) -- `Generator`: way to create an Input Set (in VASP: INCAR parameters, kpoint settings etc. ) for a computation. Additional logic was needed to adapt input sets to structures that have been changed during the workflow (e.g., volume of the structure could chance) (will be connected to the pymatgen input sets again in the future, under active development) - -### pyiron From c22ef7b81c3d8a173285200d15892a288445f08f Mon Sep 17 00:00:00 2001 From: Jan Janssen Date: Tue, 30 Apr 2024 08:59:57 -0600 Subject: [PATCH 2/5] add markdown files for introduction --- book/challenges.md | 92 +++++++++++++++++++++++++++++++++++++++ book/images/workflow.png | Bin 0 -> 186140 bytes book/introduction.md | 2 + 3 files changed, 94 insertions(+) create mode 100644 book/challenges.md create mode 100644 book/images/workflow.png create mode 100644 book/introduction.md diff --git a/book/challenges.md b/book/challenges.md new file mode 100644 index 0000000..5427453 --- /dev/null +++ b/book/challenges.md @@ -0,0 +1,92 @@ +# Challenges for workflow frameworks +Over the recent years a number of simulation frameworks have been developed which address various aspects of the development of simulation protocols. The general challenges are: + +* Interface with the simulation codes**: While some modern simulation codes already provide Python bindings, the majority require simulation code-specific input files, have specific variable names and the internal unit system used by a given simulation code might differ depending on the community it was developed for. +* **Access to high-performance computing (HPC) resources**: Traditional queuing systems like the Simple Linux Utility for Resource Management (SLURM) handle the execution of compute-intensive tasks. Still in the context of up-scaling simulation protocols or parameter studies the tracking of which parameters lead to a successful calculation and which calculations failed remains a manual task. +* **Efficient Data storage**: The input and output files of many simulation codes in the scientific community were intended to be human readable first and the machine readability was only a secondary concern. This resulted in a wide range of different formats of plain text files which are inefficient when the number of calculations increases. + +In the following, we compare the implementation of the same workflow in four different simulation frameworks, namely AiiDA, jobflow, pyiron and Simstack. All four of these frameworks are released as open-source software and developed in the Python programming language + +## Example Workflow +![workflow](images/workflow.png) +The comparison is focused on highlighting the implementation of a new simulation code and a simulation workflow in the four workflow frameworks. The calculation of a structure optimization followed by the calculation of the bulk modulus from fitting an energy volume curve with the quantum espresso open-source density functional theory (DFT) simulation code is chosen as an example workflow. The workflow consists of the following four steps: + +* Create a face-centred-cubic Aluminium supercell with 4 atoms and a lattice constant of 4.15A. +* Use the quantum espresso simulation code to optimize both the lattice constant and the positions in the crystal structure. +* Apply five strains ranging from -10% to +10% on the optimized structure to generate five strained structures. +* Evaluate these five structures with Quantum Espresso to calculate the energy. +* Plot the resulting energy volume curve. + +This workflow covers both a serial dependence of tasks as well as parallel execution of tasks. Rather than using the already existing framework-specific parsers to write the input files and parse the output files for the quantum espresso simulation code the same parser is implemented in all frameworks. This also highlights how new parsers can be developed in a general way to simplify the integration in existing simulation frameworks and enhance the parser's transferability. + +## Implementation +The challenge is to develop a workflow in a way that is can be easily integrated in a number of workflow frameworks, to give the users the option to choose the workflow framework which best suits their needs. + +### Python Function +For the case of python functions, integrating the function should ideally be as simple as setting a python decorator: +```python +@job_decorator +def my_function(*args, **kwargs): + ... +``` + +The generation of the strained structures can be represented as such a python function. It takes a single structure and a list of strains as an input and returns a list of strained structures as an output: +```python +def generate_structures(structure, strain_lst): + structure_lst = [] + for strain in strain_lst: + structure_strain = structure.copy() + structure_strain.set_cell( + structure_strain.cell * strain**(1/3), + scale_atoms=True + ) + structure_lst.append(structure_strain) + return structure_lst +``` + +In the same way, the plotting of the resulting energy volume curve can be represented as python functions: +```python +def plot_energy_volume_curve(volume_lst, energy_lst): + plt.plot(volume_lst, energy_lst) + plt.xlabel("Volume") + plt.ylabel("Energy") + plt.savefig("evcurve.png") +``` + +### External Executable +For the case of interfacing with an external executable three steps are required. The writing of the input files, the calling of the executable and the parsing of the output files. For interfacing with the quantum espresso DFT simulation code, the function to write the input files could be written as: +```python +def write_input(input_dict, working_directory="."): + filename = os.path.join(working_directory, 'input.pwi') + os.makedirs(working_directory, exist_ok=True) + write( + filename=filename, + images=input_dict["structure"], + Crystal=True, + kpts=input_dict["kpts"], + input_data={ + 'calculation': input_dict["calculation"], + 'occupations': 'smearing', + 'degauss': input_dict["smearing"], + }, + pseudopotentials=input_dict["pseudopotentials"], + tstress=True, + tprnfor=True + ) +``` + +Here all quantum espresso-specific inputs are defined by the `input_dict` and the additional `working_directory` specifies the directory in which the input should be written. In analogy, the function to collect the output of the quantum espresso calculation also receives the `working_directory` as input and returns the output as a python dictionary: +```python +def collect_output(working_directory="."): + output = parse_pw(os.path.join(working_directory, "pwscf.xml")) + return { + "structure": output["ase_structure"], + "energy": output["energy"], + "volume": output["ase_structure"].get_volume(), + } +``` + +By choosing to define both the input as well as the output as python dictionary, the interface is very flexible as it can be extended by additional elements. + +### Universal Interface +By following this recommendation of using python functions as well as external executables which are interfaced by one function to write the input and another function to parse the output any simulation workflow can be integrated in any of the four frameworks. 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z6khWjlLaQrs%-@fBUH;i+LbKEtFj?lbAx`b%NaZI8*HCok&uDa=`g@1_2grU*7!08 z3#T9zTgV-{w)C>{MTvQ3Fx0X1AQ6@Fw18e{0n!n|KQ>klLE)bUH&6CMvJQA#a1uM-A*qF-~IJ z8 Date: Tue, 30 Apr 2024 11:25:11 -0600 Subject: [PATCH 3/5] continous integration --- .github/workflows/book.yml | 28 ++++++++++++++++++++++++++++ .github/workflows/delpoy.yml | 35 +++++++++++++++++++++++++++++++++++ book/_config.yml | 17 +++++++++++++++++ book/_toc.yml | 8 ++++++++ book/environment.yml | 4 ++++ book/logo_dark.png | Bin 0 -> 66938 bytes 6 files changed, 92 insertions(+) create mode 100644 .github/workflows/book.yml create mode 100644 .github/workflows/delpoy.yml create mode 100644 book/_config.yml create mode 100644 book/_toc.yml create mode 100644 book/environment.yml create mode 100644 book/logo_dark.png diff --git a/.github/workflows/book.yml b/.github/workflows/book.yml new file mode 100644 index 0000000..ec37da0 --- /dev/null +++ b/.github/workflows/book.yml @@ -0,0 +1,28 @@ +name: Jupyterbook + +on: + pull_request: + branches: [ main ] + +jobs: + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - uses: conda-incubator/setup-miniconda@v2 + with: + auto-update-conda: true + python-version: "3.11" + environment-file: book/environment.yml + auto-activate-base: false + - name: Install Jupyterbook + shell: bash -l {0} + run: | + cp book/logo_dark.png . + cp book/_config.yml . + cp book/_toc.yml . + cp book/*.md . + cp -r book/images . + jupyter-book build . --path-output public + - run: mv public/_build/html public_html + - run: touch public_html/.nojekyll diff --git a/.github/workflows/delpoy.yml b/.github/workflows/delpoy.yml new file mode 100644 index 0000000..a2f1238 --- /dev/null +++ b/.github/workflows/delpoy.yml @@ -0,0 +1,35 @@ +name: Deploy + +on: + pull_request: + branches: [ main ] + +jobs: + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - uses: conda-incubator/setup-miniconda@v2 + with: + auto-update-conda: true + python-version: "3.11" + environment-file: book/environment.yml + auto-activate-base: false + - name: Install Jupyterbook + shell: bash -l {0} + run: | + cp book/logo_dark.png . + cp book/_config.yml . + cp book/_toc.yml . + cp book/*.md . + cp -r book/images . + jupyter-book build . --path-output public + - run: mv public/_build/html public_html + - run: touch public_html/.nojekyll + - name: Deploy 🚀 + uses: JamesIves/github-pages-deploy-action@3.7.1 + with: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + BRANCH: gh-pages # The branch the action should deploy to. + FOLDER: public_html # The folder the action should deploy. + CLEAN: true diff --git a/book/_config.yml b/book/_config.yml new file mode 100644 index 0000000..6e9fb3a --- /dev/null +++ b/book/_config.yml @@ -0,0 +1,17 @@ +title: ADIS2023 +author: pyiron +logo: logo_dark.png + +execute: + execute_notebooks : off + +html: + extra_navbar : Powered by pyiron + +repository: + url : https://github.com/materialdigital/ADIS2023 + path_to_book : "" + +launch_buttons: + notebook_interface : jupyterlab + binderhub_url : https://mybinder.org diff --git a/book/_toc.yml b/book/_toc.yml new file mode 100644 index 0000000..2baa69b --- /dev/null +++ b/book/_toc.yml @@ -0,0 +1,8 @@ +format: jb-book +root: README +chapters: +- file: introduction.md +- file: challenges.md +- file: aiida.ipynb +- file: jobflow.ipynb +- file: pyiron_base.ipynb diff --git a/book/environment.yml b/book/environment.yml new file mode 100644 index 0000000..a3fb454 --- /dev/null +++ b/book/environment.yml @@ -0,0 +1,4 @@ +channels: +- conda-forge +dependencies: +- jupyter-book diff --git a/book/logo_dark.png b/book/logo_dark.png new file mode 100644 index 0000000000000000000000000000000000000000..a601086e5383ad7bc0f903191f2918ce1022d9c2 GIT binary patch literal 66938 zcmdS=c{r7A+dqu2WtqYtzp}@Br6K;RYto;a<*)E_`aLRLY@Buz`;9Mhg8Tw6`}zOp zQ2*l-|9Sks7J|6{&r3ob{r^ud>+Apjf8EFDe_SIlNDB*s`{9fk2Zme^&`( z*FeAE0B2V%EJ%<4P;f-KC|&djb@jH;^Z=m-f;Lq6PD{#2{{K9-{J%cd@bmTyxCDmi 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deletions(-) diff --git a/.github/workflows/delpoy.yml b/.github/workflows/delpoy.yml index a2f1238..977d318 100644 --- a/.github/workflows/delpoy.yml +++ b/.github/workflows/delpoy.yml @@ -1,7 +1,7 @@ name: Deploy on: - pull_request: + push: branches: [ main ] jobs: diff --git a/aiida.ipynb b/aiida.ipynb index c7641b7..78a042f 100644 --- a/aiida.ipynb +++ b/aiida.ipynb @@ -1 +1 @@ -{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3 (ipykernel)","language":"python"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.0"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Setup for running Quantum ESPRESSO","metadata":{}},{"cell_type":"code","source":"import subprocess","metadata":{"tags":[],"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"try: \n subprocess.check_output([\"verdi\", \"profile\", \"setup\", \"core.sqlite_dos\", \"-n\", \"--profile\", \"test\", \"--email\", \"no@email.com\"])\nexcept: \n pass","metadata":{"tags":[],"trusted":true},"execution_count":2,"outputs":[{"name":"stderr","text":"/srv/conda/envs/notebook/lib/python3.11/site-packages/aiida/manage/configuration/settings.py:59: UserWarning: Creating AiiDA configuration folder `/home/jovyan/.aiida`.\n warnings.warn(f'Creating AiiDA configuration folder `{path}`.')\n","output_type":"stream"}]},{"cell_type":"code","source":"from pathlib import Path\nfrom ase.build import bulk\n\nfrom aiida import orm, engine, load_profile\nfrom aiida.common.exceptions import NotExistent\n\nload_profile()","metadata":{"tags":[],"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"Profile"},"metadata":{}}]},{"cell_type":"code","source":"try:\n localhost = orm.load_computer('localhost')\nexcept NotExistent:\n localhost = orm.Computer(\n label='localhost',\n hostname='localhost',\n transport_type='core.local',\n scheduler_type='core.direct',\n workdir=Path('workdir').absolute().as_posix()\n ).store()\n localhost.configure()\n\ntry:\n pw_code = orm.load_code('pw@localhost')\nexcept NotExistent:\n pw_code = orm.InstalledCode(\n label='pw',\n computer=localhost,\n filepath_executable='pw.x',\n default_calc_job_plugin='aiida_qe_basic.pw',\n prepend_text='export OMP_NUM_THREADS=1'\n ).store()","metadata":{"tags":[],"trusted":true},"execution_count":4,"outputs":[{"name":"stderr","text":"/srv/conda/envs/notebook/lib/python3.11/site-packages/aiida/orm/nodes/data/code/legacy.py:42: AiidaDeprecationWarning: The `Code` class is deprecated. To create an instance, use the `aiida.orm.nodes.data.code.installed.InstalledCode` or `aiida.orm.nodes.data.code.portable.PortableCode` for a \"remote\" or \"local\" code, respectively. If you are using this class to compare type, e.g. in `isinstance`, use `aiida.orm.nodes.data.code.abstract.AbstractCode`. (this will be removed in v3)\n warn_deprecation(\n","output_type":"stream"}]},{"cell_type":"code","source":"from aiida_qe_basic.pw import PwCalculation\n\nbuilder = PwCalculation.get_builder()\n\nbuilder.code = pw_code\nbuilder.structure = orm.StructureData(ase=bulk('Al', a=4.05, cubic=True))\nbuilder.pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\nbuilder.parameters = orm.Dict(\n {\n 'CONTROL': {\n 'calculation': 'scf',\n # 'pseudo_dir': Path('files').absolute().as_posix(),\n },\n 'SYSTEM': {\n 'occupations': 'smearing',\n 'smearing': 'cold',\n 'degauss': 0.02\n }\n }\n)\nbuilder.metadata.options.resources = {\n 'num_machines': 1,\n 'num_mpiprocs_per_machine': 1\n}","metadata":{"tags":[],"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"! rabbitmq-server -detached","metadata":{"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"! sleep 5","metadata":{"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"results = engine.run(builder)","metadata":{"tags":[],"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"results","metadata":{"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"{'structure': ,\n 'properties': ,\n 'remote_folder': ,\n 'retrieved': }"},"metadata":{}}]},{"cell_type":"code","source":"results['properties'].get_dict()","metadata":{"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"{'energy': -1074.9272223013, 'volume': 66.430124128914}"},"metadata":{}}]},{"cell_type":"markdown","source":"# Equation of State curve - basic QE\n\nRunning an EOS without all the fancy features in the `aiida-quantumespresso` plugin.","metadata":{}},{"cell_type":"code","source":"from pathlib import Path\n\nfrom aiida import orm, engine, load_profile\n\nload_profile()","metadata":{"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"Profile"},"metadata":{}}]},{"cell_type":"markdown","source":"## Importing a structure","metadata":{}},{"cell_type":"code","source":"from ase.build import bulk\n\nstructure = orm.StructureData(ase=bulk('Al', a=4.05, cubic=True))","metadata":{"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"markdown","source":"## Relaxing the geometry","metadata":{}},{"cell_type":"code","source":"resources = {\n 'num_machines': 1,\n 'num_mpiprocs_per_machine': 1\n}\n\nrelax_params = {\n 'CONTROL': {\n 'calculation': 'vc-relax',\n # 'pseudo_dir': Path('files').absolute().as_posix(),\n },\n 'SYSTEM': {\n 'occupations': 'smearing',\n 'smearing': 'cold',\n 'degauss': 0.02\n }\n}","metadata":{"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"from aiida_qe_basic.pw import PwCalculation\n\nbuilder = PwCalculation.get_builder()\n\nbuilder.code = orm.load_code('pw@localhost')\nbuilder.structure = orm.StructureData(ase=bulk('Al', a=4.05, cubic=True))\nbuilder.pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\nbuilder.parameters = orm.Dict(relax_params)\nbuilder.metadata.options.resources = resources","metadata":{"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"results = engine.run(builder)\nrelaxed_structure = results['structure']\nrelaxed_structure","metadata":{"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}}]},{"cell_type":"markdown","source":"## Calc function to rescale structures\n\nThe `calcfunction` below takes an input structure and rescales it to different volumes.","metadata":{}},{"cell_type":"code","source":"from aiida_qe_basic.pw import PwCalculation\n\n@engine.calcfunction\ndef rescale_list(structure: orm.StructureData, factor_list: orm.List):\n\n scaled_structure_dict = {}\n\n for index, scaling_factor in enumerate(factor_list.get_list()):\n\n ase_structure = structure.get_ase()\n\n new_cell = ase_structure.get_cell() * scaling_factor\n ase_structure.set_cell(new_cell, scale_atoms=True)\n\n scaled_structure_dict[f'structure_{index}'] = orm.StructureData(ase=ase_structure)\n\n return scaled_structure_dict","metadata":{"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":"Typically, you'd just run it by calling the function as you would a regular Python function:","metadata":{}},{"cell_type":"code","source":"rescaled_structures = rescale_list(relaxed_structure, orm.List(list=[0.9, 0.95, 1.0, 1.05, 1.1]))","metadata":{"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"rescaled_structures","metadata":{"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"{'structure_0': ,\n 'structure_1': ,\n 'structure_2': ,\n 'structure_3': ,\n 'structure_4': }"},"metadata":{}}]},{"cell_type":"markdown","source":"## EOS: Work function version","metadata":{}},{"cell_type":"code","source":"scf_inputs = {\n 'CONTROL': {\n 'calculation': 'scf',\n # 'pseudo_dir': Path('files').absolute().as_posix(),\n },\n 'SYSTEM': {\n 'occupations': 'smearing',\n 'smearing': 'cold',\n 'degauss': 0.02\n }\n}","metadata":{"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"@engine.workfunction\ndef run_eos_wf(code: orm.Code, structure: orm.StructureData, scale_factors: orm.List):\n \"\"\"Run an equation of state of a bulk crystal structure for the given element.\"\"\"\n\n properties = {}\n\n for label, rescaled_structure in rescale_list(structure, scale_factors).items():\n\n builder = PwCalculation.get_builder()\n builder.code = code\n builder.structure = rescaled_structure\n builder.parameters = orm.Dict(scf_inputs)\n builder.pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\n builder.metadata.options.resources = resources\n\n results = engine.run(builder)\n properties[label] = results['properties']\n\n return properties","metadata":{"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"results = run_eos_wf(\n code=orm.load_code('pw@localhost'),\n structure=relaxed_structure,\n scale_factors=[0.9, 0.95, 1.0, 1.05, 1.1]\n)","metadata":{"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"results","metadata":{"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"{'structure_0': ,\n 'structure_1': ,\n 'structure_2': ,\n 'structure_3': ,\n 'structure_4': }"},"metadata":{}}]},{"cell_type":"code","source":"volumes = []\nenergies = []\n\nfor result in results.values():\n volumes.append(result['volume'])\n energies.append(result['energy'])","metadata":{"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nplt.plot(volumes, energies)","metadata":{"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"[]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
    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"},"metadata":{}}]},{"cell_type":"markdown","source":"## Work chain version","metadata":{}},{"cell_type":"code","source":"@engine.calcfunction\ndef create_eos_dictionary(**kwargs) -> orm.Dict:\n eos = {\n label: (result['volume'], result['energy'])\n for label, result in kwargs.items()\n }\n return orm.Dict(eos)","metadata":{"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"create_eos_dictionary(**results).get_dict()","metadata":{"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"{'structure_0': [48.283007573324, -1073.9421694118],\n 'structure_1': [56.785519366503, -1074.7251942208],\n 'structure_2': [66.231834805659, -1074.9273047095],\n 'structure_3': [76.671627766898, -1074.7863009779],\n 'structure_4': [88.154572126333, -1074.451028786]}"},"metadata":{}}]},{"cell_type":"code","source":"class EquationOfState(engine.WorkChain):\n \"\"\"WorkChain to compute Equation of State using Quantum ESPRESSO.\"\"\"\n\n @classmethod\n def define(cls, spec):\n \"\"\"Specify inputs and outputs.\"\"\"\n super().define(spec)\n spec.input(\"code\", valid_type=orm.Code)\n spec.input(\"structure\", valid_type=orm.StructureData)\n spec.input(\"scale_factors\", valid_type=orm.List)\n\n spec.outline(\n cls.run_eos,\n cls.results,\n )\n spec.output(\"eos_dict\", valid_type=orm.Dict)\n\n def run_eos(self):\n\n calcjob_dict = {}\n\n for label, rescaled_structure in rescale_list(self.inputs.structure, self.inputs.scale_factors).items():\n\n builder = PwCalculation.get_builder()\n builder.code = self.inputs.code\n builder.structure = rescaled_structure\n builder.parameters = orm.Dict(scf_inputs)\n builder.pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\n builder.metadata.options.resources = resources\n\n calcjob_dict[label] = self.submit(builder)\n\n self.ctx.labels = list(calcjob_dict.keys())\n\n return calcjob_dict\n\n def results(self):\n\n self.report(self.ctx)\n\n eos_results = {\n label: self.ctx[label].outputs['properties'] for label in self.ctx.labels\n }\n eos_dict = create_eos_dictionary(**eos_results)\n self.out('eos_dict', eos_dict)\n","metadata":{"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"engine.run(EquationOfState, code=orm.load_code('pw@localhost'),\n structure=relaxed_structure,\n scale_factors=orm.List([0.9, 0.95, 1.0, 1.05, 1.1]))","metadata":{"trusted":true},"execution_count":28,"outputs":[{"name":"stderr","text":"04/04/2024 05:40:39 PM <83> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [71|EquationOfState|results]: AttributeDict({'labels': ['structure_0', 'structure_1', 'structure_2', 'structure_3', 'structure_4'], 'structure_0': , 'structure_1': , 'structure_2': , 'structure_3': , 'structure_4': })\n","output_type":"stream"},{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"{'eos_dict': }"},"metadata":{}}]},{"cell_type":"markdown","source":"## Using the `builder`","metadata":{}},{"cell_type":"code","source":"builder = EquationOfState.get_builder()","metadata":{"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"builder.structure = relaxed_structure","metadata":{"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"builder","metadata":{"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"Process class: EquationOfState\nInputs:\nmetadata: {}\nstructure: Al\n"},"metadata":{}}]},{"cell_type":"code","source":"builder.scale_factors = orm.List([0.9, 0.95, 1.0, 1.05, 1.1])\nbuilder.code = orm.load_code('pw@localhost')","metadata":{"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"results, node = engine.run_get_node(builder)","metadata":{"trusted":true},"execution_count":33,"outputs":[{"name":"stderr","text":"04/04/2024 05:41:05 PM <83> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [116|EquationOfState|results]: AttributeDict({'labels': ['structure_0', 'structure_1', 'structure_2', 'structure_3', 'structure_4'], 'structure_0': , 'structure_1': , 'structure_2': , 'structure_3': , 'structure_4': })\n","output_type":"stream"}]},{"cell_type":"code","source":"results['eos_dict'].get_dict()","metadata":{"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"{'structure_0': [48.283007573324, -1073.9421694118],\n 'structure_1': [56.785519366503, -1074.7251942208],\n 'structure_2': [66.231834805659, -1074.9273047095],\n 'structure_3': [76.671627766898, -1074.7863009779],\n 'structure_4': [88.154572126333, -1074.451028786]}"},"metadata":{}}]},{"cell_type":"code","source":"eos = node.outputs.eos_dict.get_dict()","metadata":{"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"eos","metadata":{"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"{'structure_0': [48.283007573324, -1073.9421694118],\n 'structure_1': [56.785519366503, -1074.7251942208],\n 'structure_2': [66.231834805659, -1074.9273047095],\n 'structure_3': [76.671627766898, -1074.7863009779],\n 'structure_4': [88.154572126333, -1074.451028786]}"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(\n [v[0] for v in eos.values()],\n [v[1] for v in eos.values()],\n)","metadata":{"tags":[],"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"[]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
    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"},"metadata":{}}]}]} \ No newline at end of file +{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3 (ipykernel)","language":"python"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.0"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Aiida","metadata":{}},{"cell_type":"code","source":"import subprocess","metadata":{"tags":[]},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"try: \n subprocess.check_output([\"verdi\", \"profile\", \"setup\", \"core.sqlite_dos\", \"-n\", \"--profile\", \"test\", \"--email\", \"no@email.com\"])\nexcept: \n pass","metadata":{"tags":[]},"execution_count":2,"outputs":[{"name":"stderr","text":"/srv/conda/envs/notebook/lib/python3.11/site-packages/aiida/manage/configuration/settings.py:59: UserWarning: Creating AiiDA configuration folder `/home/jovyan/.aiida`.\n warnings.warn(f'Creating AiiDA configuration folder `{path}`.')\n","output_type":"stream"}]},{"cell_type":"code","source":"from pathlib import Path\nfrom ase.build import bulk\n\nfrom aiida import orm, engine, load_profile\nfrom aiida.common.exceptions import NotExistent\n\nload_profile()","metadata":{"tags":[]},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"Profile"},"metadata":{}}]},{"cell_type":"code","source":"try:\n localhost = orm.load_computer('localhost')\nexcept NotExistent:\n localhost = orm.Computer(\n label='localhost',\n hostname='localhost',\n transport_type='core.local',\n scheduler_type='core.direct',\n workdir=Path('workdir').absolute().as_posix()\n ).store()\n localhost.configure()\n\ntry:\n pw_code = orm.load_code('pw@localhost')\nexcept NotExistent:\n pw_code = orm.InstalledCode(\n label='pw',\n computer=localhost,\n filepath_executable='pw.x',\n default_calc_job_plugin='aiida_qe_basic.pw',\n prepend_text='export OMP_NUM_THREADS=1'\n ).store()","metadata":{"tags":[]},"execution_count":4,"outputs":[{"name":"stderr","text":"/srv/conda/envs/notebook/lib/python3.11/site-packages/aiida/orm/nodes/data/code/legacy.py:42: AiidaDeprecationWarning: The `Code` class is deprecated. To create an instance, use the `aiida.orm.nodes.data.code.installed.InstalledCode` or `aiida.orm.nodes.data.code.portable.PortableCode` for a \"remote\" or \"local\" code, respectively. If you are using this class to compare type, e.g. in `isinstance`, use `aiida.orm.nodes.data.code.abstract.AbstractCode`. (this will be removed in v3)\n warn_deprecation(\n","output_type":"stream"}]},{"cell_type":"code","source":"from aiida_qe_basic.pw import PwCalculation\n\nbuilder = PwCalculation.get_builder()\n\nbuilder.code = pw_code\nbuilder.structure = orm.StructureData(ase=bulk('Al', a=4.05, cubic=True))\nbuilder.pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\nbuilder.parameters = orm.Dict(\n {\n 'CONTROL': {\n 'calculation': 'scf',\n # 'pseudo_dir': Path('files').absolute().as_posix(),\n },\n 'SYSTEM': {\n 'occupations': 'smearing',\n 'smearing': 'cold',\n 'degauss': 0.02\n }\n }\n)\nbuilder.metadata.options.resources = {\n 'num_machines': 1,\n 'num_mpiprocs_per_machine': 1\n}","metadata":{"tags":[]},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"! rabbitmq-server -detached","metadata":{},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"! sleep 5","metadata":{},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"results = engine.run(builder)","metadata":{"tags":[]},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"results","metadata":{},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"{'structure': ,\n 'properties': ,\n 'remote_folder': ,\n 'retrieved': }"},"metadata":{}}]},{"cell_type":"code","source":"results['properties'].get_dict()","metadata":{},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"{'energy': -1074.9272223013, 'volume': 66.430124128914}"},"metadata":{}}]},{"cell_type":"markdown","source":"## Equation of State curve - basic QE\n\nRunning an EOS without all the fancy features in the `aiida-quantumespresso` plugin.","metadata":{}},{"cell_type":"code","source":"from pathlib import Path\n\nfrom aiida import orm, engine, load_profile\n\nload_profile()","metadata":{},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"Profile"},"metadata":{}}]},{"cell_type":"markdown","source":"### Importing a structure","metadata":{}},{"cell_type":"code","source":"from ase.build import bulk\n\nstructure = orm.StructureData(ase=bulk('Al', a=4.05, cubic=True))","metadata":{},"execution_count":12,"outputs":[]},{"cell_type":"markdown","source":"### Relaxing the geometry","metadata":{}},{"cell_type":"code","source":"resources = {\n 'num_machines': 1,\n 'num_mpiprocs_per_machine': 1\n}\n\nrelax_params = {\n 'CONTROL': {\n 'calculation': 'vc-relax',\n # 'pseudo_dir': Path('files').absolute().as_posix(),\n },\n 'SYSTEM': {\n 'occupations': 'smearing',\n 'smearing': 'cold',\n 'degauss': 0.02\n }\n}","metadata":{},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"from aiida_qe_basic.pw import PwCalculation\n\nbuilder = PwCalculation.get_builder()\n\nbuilder.code = orm.load_code('pw@localhost')\nbuilder.structure = orm.StructureData(ase=bulk('Al', a=4.05, cubic=True))\nbuilder.pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\nbuilder.parameters = orm.Dict(relax_params)\nbuilder.metadata.options.resources = resources","metadata":{},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"results = engine.run(builder)\nrelaxed_structure = results['structure']\nrelaxed_structure","metadata":{},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}}]},{"cell_type":"markdown","source":"### Calc function to rescale structures\n\nThe `calcfunction` below takes an input structure and rescales it to different volumes.","metadata":{}},{"cell_type":"code","source":"from aiida_qe_basic.pw import PwCalculation\n\n@engine.calcfunction\ndef rescale_list(structure: orm.StructureData, factor_list: orm.List):\n\n scaled_structure_dict = {}\n\n for index, scaling_factor in enumerate(factor_list.get_list()):\n\n ase_structure = structure.get_ase()\n\n new_cell = ase_structure.get_cell() * scaling_factor\n ase_structure.set_cell(new_cell, scale_atoms=True)\n\n scaled_structure_dict[f'structure_{index}'] = orm.StructureData(ase=ase_structure)\n\n return scaled_structure_dict","metadata":{},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":"Typically, you'd just run it by calling the function as you would a regular Python function:","metadata":{}},{"cell_type":"code","source":"rescaled_structures = rescale_list(relaxed_structure, orm.List(list=[0.9, 0.95, 1.0, 1.05, 1.1]))","metadata":{},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"rescaled_structures","metadata":{},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"{'structure_0': ,\n 'structure_1': ,\n 'structure_2': ,\n 'structure_3': ,\n 'structure_4': }"},"metadata":{}}]},{"cell_type":"markdown","source":"## EOS: Work function version","metadata":{}},{"cell_type":"code","source":"scf_inputs = {\n 'CONTROL': {\n 'calculation': 'scf',\n # 'pseudo_dir': Path('files').absolute().as_posix(),\n },\n 'SYSTEM': {\n 'occupations': 'smearing',\n 'smearing': 'cold',\n 'degauss': 0.02\n }\n}","metadata":{},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"@engine.workfunction\ndef run_eos_wf(code: orm.Code, structure: orm.StructureData, scale_factors: orm.List):\n \"\"\"Run an equation of state of a bulk crystal structure for the given element.\"\"\"\n\n properties = {}\n\n for label, rescaled_structure in rescale_list(structure, scale_factors).items():\n\n builder = PwCalculation.get_builder()\n builder.code = code\n builder.structure = rescaled_structure\n builder.parameters = orm.Dict(scf_inputs)\n builder.pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\n builder.metadata.options.resources = resources\n\n results = engine.run(builder)\n properties[label] = results['properties']\n\n return properties","metadata":{},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"results = run_eos_wf(\n code=orm.load_code('pw@localhost'),\n structure=relaxed_structure,\n scale_factors=[0.9, 0.95, 1.0, 1.05, 1.1]\n)","metadata":{},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"results","metadata":{},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"{'structure_0': ,\n 'structure_1': ,\n 'structure_2': ,\n 'structure_3': ,\n 'structure_4': }"},"metadata":{}}]},{"cell_type":"code","source":"volumes = []\nenergies = []\n\nfor result in results.values():\n volumes.append(result['volume'])\n energies.append(result['energy'])","metadata":{},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nplt.plot(volumes, energies)","metadata":{},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"[]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
    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"},"metadata":{}}]},{"cell_type":"markdown","source":"## Work chain version","metadata":{}},{"cell_type":"code","source":"@engine.calcfunction\ndef create_eos_dictionary(**kwargs) -> orm.Dict:\n eos = {\n label: (result['volume'], result['energy'])\n for label, result in kwargs.items()\n }\n return orm.Dict(eos)","metadata":{},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"create_eos_dictionary(**results).get_dict()","metadata":{},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"{'structure_0': [48.283007573324, -1073.9421694118],\n 'structure_1': [56.785519366503, -1074.7251942208],\n 'structure_2': [66.231834805659, -1074.9273047095],\n 'structure_3': [76.671627766898, -1074.7863009779],\n 'structure_4': [88.154572126333, -1074.451028786]}"},"metadata":{}}]},{"cell_type":"code","source":"class EquationOfState(engine.WorkChain):\n \"\"\"WorkChain to compute Equation of State using Quantum ESPRESSO.\"\"\"\n\n @classmethod\n def define(cls, spec):\n \"\"\"Specify inputs and outputs.\"\"\"\n super().define(spec)\n spec.input(\"code\", valid_type=orm.Code)\n spec.input(\"structure\", valid_type=orm.StructureData)\n spec.input(\"scale_factors\", valid_type=orm.List)\n\n spec.outline(\n cls.run_eos,\n cls.results,\n )\n spec.output(\"eos_dict\", valid_type=orm.Dict)\n\n def run_eos(self):\n\n calcjob_dict = {}\n\n for label, rescaled_structure in rescale_list(self.inputs.structure, self.inputs.scale_factors).items():\n\n builder = PwCalculation.get_builder()\n builder.code = self.inputs.code\n builder.structure = rescaled_structure\n builder.parameters = orm.Dict(scf_inputs)\n builder.pseudopotentials = orm.Dict({\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\n builder.metadata.options.resources = resources\n\n calcjob_dict[label] = self.submit(builder)\n\n self.ctx.labels = list(calcjob_dict.keys())\n\n return calcjob_dict\n\n def results(self):\n\n self.report(self.ctx)\n\n eos_results = {\n label: self.ctx[label].outputs['properties'] for label in self.ctx.labels\n }\n eos_dict = create_eos_dictionary(**eos_results)\n self.out('eos_dict', eos_dict)\n","metadata":{},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"engine.run(EquationOfState, code=orm.load_code('pw@localhost'),\n structure=relaxed_structure,\n scale_factors=orm.List([0.9, 0.95, 1.0, 1.05, 1.1]))","metadata":{},"execution_count":28,"outputs":[{"name":"stderr","text":"04/04/2024 05:40:39 PM <83> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [71|EquationOfState|results]: AttributeDict({'labels': ['structure_0', 'structure_1', 'structure_2', 'structure_3', 'structure_4'], 'structure_0': , 'structure_1': , 'structure_2': , 'structure_3': , 'structure_4': })\n","output_type":"stream"},{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"{'eos_dict': }"},"metadata":{}}]},{"cell_type":"markdown","source":"## Using the `builder`","metadata":{}},{"cell_type":"code","source":"builder = EquationOfState.get_builder()","metadata":{},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"builder.structure = relaxed_structure","metadata":{},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"builder","metadata":{},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"Process class: EquationOfState\nInputs:\nmetadata: {}\nstructure: Al\n"},"metadata":{}}]},{"cell_type":"code","source":"builder.scale_factors = orm.List([0.9, 0.95, 1.0, 1.05, 1.1])\nbuilder.code = orm.load_code('pw@localhost')","metadata":{},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"results, node = engine.run_get_node(builder)","metadata":{},"execution_count":33,"outputs":[{"name":"stderr","text":"04/04/2024 05:41:05 PM <83> aiida.orm.nodes.process.workflow.workchain.WorkChainNode: [REPORT] [116|EquationOfState|results]: AttributeDict({'labels': ['structure_0', 'structure_1', 'structure_2', 'structure_3', 'structure_4'], 'structure_0': , 'structure_1': , 'structure_2': , 'structure_3': , 'structure_4': })\n","output_type":"stream"}]},{"cell_type":"code","source":"results['eos_dict'].get_dict()","metadata":{},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"{'structure_0': [48.283007573324, -1073.9421694118],\n 'structure_1': [56.785519366503, -1074.7251942208],\n 'structure_2': [66.231834805659, -1074.9273047095],\n 'structure_3': [76.671627766898, -1074.7863009779],\n 'structure_4': [88.154572126333, -1074.451028786]}"},"metadata":{}}]},{"cell_type":"code","source":"eos = node.outputs.eos_dict.get_dict()","metadata":{},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"eos","metadata":{},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"{'structure_0': [48.283007573324, -1073.9421694118],\n 'structure_1': [56.785519366503, -1074.7251942208],\n 'structure_2': [66.231834805659, -1074.9273047095],\n 'structure_3': [76.671627766898, -1074.7863009779],\n 'structure_4': [88.154572126333, -1074.451028786]}"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(\n [v[0] for v in eos.values()],\n [v[1] for v in eos.values()],\n)","metadata":{"tags":[]},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"[]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
    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"},"metadata":{}}]}]} \ No newline at end of file diff --git a/jobflow.ipynb b/jobflow.ipynb index 567dd3d..7778775 100644 --- a/jobflow.ipynb +++ b/jobflow.ipynb @@ -1 +1 @@ -{"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.11.8","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import subprocess\nimport os\nfrom pydantic import BaseModel, Field","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:39.230259Z","start_time":"2024-04-04T12:23:39.139154Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:39.812727Z","start_time":"2024-04-04T12:23:39.233298Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"from ase.build import bulk\nfrom ase.io import write","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.064521Z","start_time":"2024-04-04T12:23:39.815771Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"from adis_tools.parsers import parse_pw","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.342218Z","start_time":"2024-04-04T12:23:40.067769Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"# Functions","metadata":{}},{"cell_type":"code","source":"def generate_structures(structure, strain_lst): \n structure_lst = []\n for strain in strain_lst:\n structure_strain = structure.copy()\n structure_strain.set_cell(\n structure_strain.cell * strain**(1/3), \n scale_atoms=True\n )\n structure_lst.append(structure_strain)\n return structure_lst","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.349810Z","start_time":"2024-04-04T12:23:40.344659Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"def plot_energy_volume_curve(volume_lst, energy_lst):\n plt.plot(volume_lst, energy_lst)\n plt.xlabel(\"Volume\")\n plt.ylabel(\"Energy\")\n plt.savefig(\"evcurve.png\")","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.354972Z","start_time":"2024-04-04T12:23:40.351547Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"def write_input(input_dict, working_directory=\".\"):\n filename = os.path.join(working_directory, 'input.pwi')\n os.makedirs(working_directory, exist_ok=True)\n write(\n filename=filename, \n images=input_dict[\"structure\"], \n Crystal=True, \n kpts=input_dict[\"kpts\"], \n input_data={\n 'calculation': input_dict[\"calculation\"],\n 'occupations': 'smearing',\n 'degauss': input_dict[\"smearing\"],\n }, \n pseudopotentials=input_dict[\"pseudopotentials\"],\n tstress=True, \n tprnfor=True\n )","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.361062Z","start_time":"2024-04-04T12:23:40.356433Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"def collect_output(working_directory=\".\"):\n output = parse_pw(os.path.join(working_directory, 'pwscf.xml'))\n return {\n \"structure\": output['ase_structure'],\n \"energy\": output[\"energy\"],\n \"volume\": output['ase_structure'].get_volume(),\n }","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.365984Z","start_time":"2024-04-04T12:23:40.362667Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"from pymatgen.io.core import InputSet, InputGenerator\nfrom pymatgen.io.ase import MSONAtoms\nfrom typing import Any, Optional, Union\nQE_CMD= \"mpirun -np 1 pw.x -in input.pwi > output.pwo\"\ndef run_qe(qe_cmd=QE_CMD):\n subprocess.check_output(qe_cmd, shell=True, universal_newlines=True)\n\nclass QETaskDoc(BaseModel):\n structure: Optional[MSONAtoms] = Field(None, description=\"ASE structure\")\n energy: Optional[float] = Field(None, description=\"DFT energy in eV\")\n volume: Optional[float] = Field(None, description=\"volume in Angstrom^3\")\n \n @classmethod\n def from_directory(cls, working_directory):\n output=collect_output(working_directory=working_directory)\n # structure object needs to be serializable, i.e., we need an additional transformation\n return cls(structure=MSONAtoms(output[\"structure\"]), energy=output[\"energy\"], volume=output[\"volume\"])\n\nclass QEInputSet(InputSet):\n \"\"\"\n Writes an input based on an input_dict\n \"\"\"\n def __init__(self, input_dict):\n self.input_dict = input_dict\n\n def write_input(self, working_directory=\".\"):\n write_input(self.input_dict, working_directory=working_directory)\n\nfrom dataclasses import dataclass, field\n\n\n\n@dataclass\nclass QEInputGenerator(InputGenerator):\n pseudopotentials: dict = field(default_factory=lambda: {\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\n kpts: tuple = (3,3,3)\n calculation: str = \"vc-relax\"\n smearing: float = 0.02\n \n\n def get_input_set(self, structure) -> QEInputSet:\n\n input_dict={\"structure\":structure,\n \"pseudopotentials\":self.pseudopotentials, \n \"kpts\": self.kpts,\n \"calculation\": self.calculation,\n \"smearing\": self.smearing,\n }\n return QEInputSet(input_dict=input_dict)\n\n@dataclass\nclass QEInputStaticGenerator(QEInputGenerator):\n calculation: str = \"scf\"\n\n \ndef write_qe_input_set(structure, input_set_generator=QEInputGenerator(), working_directory=\".\"):\n qis = input_set_generator.get_input_set(structure=structure)\n qis.write_input(working_directory=working_directory)\n \n ","metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false},"ExecuteTime":{"end_time":"2024-04-04T12:23:40.992461Z","start_time":"2024-04-04T12:23:40.368632Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"from dataclasses import dataclass, field\nfrom jobflow import job, Maker\n\n\n@dataclass\nclass BaseQEMaker(Maker):\n \"\"\"\n Base QE job maker.\n\n Parameters\n ----------\n name : str\n The job name.\n input_set_generator : .QEInputGenerator\n A generator used to make the input set.\n \"\"\"\n\n name: str = \"base qe job\"\n input_set_generator: QEInputGenerator = field(default_factory=QEInputGenerator)\n\n @job(output_schema=QETaskDoc)\n def make(\n self, structure\n ) -> QETaskDoc:\n \"\"\"\n Run a QE calculation.\n\n Parameters\n ----------\n structure : MSONAtoms|Atoms\n An Atoms or MSONAtoms object.\n \n Returns\n -------\n Output of a QE calculation\n \"\"\"\n # copy previous inputs\n\n # write qe input files\n write_qe_input_set(\n structure=structure, input_set_generator=self.input_set_generator)\n\n # qe\n run_qe()\n\n # parse qe outputs\n task_doc=QETaskDoc.from_directory(\".\")\n \n return task_doc\n\n@dataclass\nclass StaticQEMaker(BaseQEMaker):\n \"\"\"\n Base QE job maker.\n\n Parameters\n ----------\n name : str\n The job name.\n input_set_generator : .QEInputGenerator\n A generator used to make the input set.\n \"\"\"\n\n name: str = \"static qe job\"\n input_set_generator: QEInputGenerator = field(default_factory=QEInputStaticGenerator)\n\n\n\nfrom jobflow import job, Response, Flow, run_locally\n\n@job\ndef get_ev_curve(structure, strain_lst):\n structures=generate_structures(structure,strain_lst=strain_lst)\n jobs = []\n volumes = []\n energies = []\n for istructure in range(len(strain_lst)):\n new_job = StaticQEMaker().make(structures[istructure])\n jobs.append(new_job)\n volumes.append(new_job.output.volume)\n energies.append(new_job.output.energy)\n return Response(replace=Flow(jobs, output={\"energies\": energies, \"volumes\": volumes}))\n \n@job\ndef plot_energy_volume_curve_job(volume_lst, energy_lst):\n plot_energy_volume_curve(volume_lst=volume_lst, energy_lst=energy_lst)\n\nstructure = bulk('Al', a=4.15, cubic=True)\nrelax = BaseQEMaker().make(structure=MSONAtoms(structure))\nev_curve = get_ev_curve(relax.output.structure, strain_lst=np.linspace(0.9, 1.1, 5))\nplot = plot_energy_volume_curve_job(volume_lst=ev_curve.output[\"volumes\"], energy_lst=ev_curve.output[\"energies\"])\njobs = [relax, ev_curve, plot]\nrun_locally(Flow(jobs), create_folders=True)","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:26:08.108635Z","start_time":"2024-04-04T12:24:48.120617Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"2024-04-04 15:55:32,815 INFO Started executing jobs locally\n2024-04-04 15:55:32,892 INFO Starting job - base qe job (72ab2547-8a66-4dd0-a95a-b6255a668cd8)\n","output_type":"stream"},{"name":"stderr","text":"[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00139] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:56:37,395 INFO Finished job - base qe job (72ab2547-8a66-4dd0-a95a-b6255a668cd8)\n2024-04-04 15:56:37,396 INFO Starting job - get_ev_curve (87ad6b9b-9f34-463e-b705-1ab7cdcf9aee)\n2024-04-04 15:56:37,404 INFO Finished job - get_ev_curve (87ad6b9b-9f34-463e-b705-1ab7cdcf9aee)\n2024-04-04 15:56:37,406 INFO Starting job - static qe job (b0759785-155b-4e79-9b72-c626067d81e1)\n","output_type":"stream"},{"name":"stderr","text":"[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00149] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:56:45,563 INFO Finished job - static qe job (b0759785-155b-4e79-9b72-c626067d81e1)\n2024-04-04 15:56:45,564 INFO Starting job - static qe job (514079e9-d6ae-4369-8b80-bf8ca7860540)\n","output_type":"stream"},{"name":"stderr","text":"Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00159] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:56:53,979 INFO Finished job - static qe job (514079e9-d6ae-4369-8b80-bf8ca7860540)\n2024-04-04 15:56:53,980 INFO Starting job - static qe job (6e3c5a28-4edb-4244-9e35-0b8733b946c4)\n","output_type":"stream"},{"name":"stderr","text":"[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00169] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:57:03,600 INFO Finished job - static qe job (6e3c5a28-4edb-4244-9e35-0b8733b946c4)\n2024-04-04 15:57:03,600 INFO Starting job - static qe job (980b72a5-fbea-445c-9075-a880322c8261)\n","output_type":"stream"},{"name":"stderr","text":"Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00179] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:57:14,907 INFO Finished job - static qe job (980b72a5-fbea-445c-9075-a880322c8261)\n2024-04-04 15:57:14,908 INFO Starting job - static qe job (a44a935f-6bd2-4279-bbcb-53d5ed890a99)\n","output_type":"stream"},{"name":"stderr","text":"Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00189] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:57:27,174 INFO Finished job - static qe job (a44a935f-6bd2-4279-bbcb-53d5ed890a99)\n2024-04-04 15:57:27,174 INFO Starting job - store_inputs (87ad6b9b-9f34-463e-b705-1ab7cdcf9aee, 2)\n2024-04-04 15:57:27,176 INFO Finished job - store_inputs (87ad6b9b-9f34-463e-b705-1ab7cdcf9aee, 2)\n2024-04-04 15:57:27,176 INFO Starting job - plot_energy_volume_curve_job (9987ab9f-1ae9-4172-8e31-b2c9920d4791)\n2024-04-04 15:57:27,256 INFO Finished job - plot_energy_volume_curve_job (9987ab9f-1ae9-4172-8e31-b2c9920d4791)\n2024-04-04 15:57:27,256 INFO Finished executing jobs locally\n","output_type":"stream"},{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"{'72ab2547-8a66-4dd0-a95a-b6255a668cd8': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[4.045218941837687, 4.045218941837687, 4.045218941837687]), energy=-1074.9365272693506, volume=66.1951387021735), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '87ad6b9b-9f34-463e-b705-1ab7cdcf9aee': {1: Response(output=None, detour=None, addition=None, replace=Flow(name='Flow', uuid='0e46d480-7699-4e03-889b-b4ba65fe3d38')\n 1. Job(name='static qe job', uuid='b0759785-155b-4e79-9b72-c626067d81e1')\n 2. Job(name='static qe job', uuid='514079e9-d6ae-4369-8b80-bf8ca7860540')\n 3. Job(name='static qe job', uuid='6e3c5a28-4edb-4244-9e35-0b8733b946c4')\n 4. Job(name='static qe job', uuid='980b72a5-fbea-445c-9075-a880322c8261')\n 5. Job(name='static qe job', uuid='a44a935f-6bd2-4279-bbcb-53d5ed890a99')\n 6. Job(name='store_inputs', uuid='87ad6b9b-9f34-463e-b705-1ab7cdcf9aee'), stored_data=None, stop_children=False, stop_jobflow=False),\n 2: Response(output={'energies': [OutputReference(b0759785-155b-4e79-9b72-c626067d81e1, .energy), OutputReference(514079e9-d6ae-4369-8b80-bf8ca7860540, .energy), OutputReference(6e3c5a28-4edb-4244-9e35-0b8733b946c4, .energy), OutputReference(980b72a5-fbea-445c-9075-a880322c8261, .energy), OutputReference(a44a935f-6bd2-4279-bbcb-53d5ed890a99, .energy)], 'volumes': [OutputReference(b0759785-155b-4e79-9b72-c626067d81e1, .volume), OutputReference(514079e9-d6ae-4369-8b80-bf8ca7860540, .volume), OutputReference(6e3c5a28-4edb-4244-9e35-0b8733b946c4, .volume), OutputReference(980b72a5-fbea-445c-9075-a880322c8261, .volume), OutputReference(a44a935f-6bd2-4279-bbcb-53d5ed890a99, .volume)]}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n 'b0759785-155b-4e79-9b72-c626067d81e1': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[3.9056159296787105, 3.9056159296787105, 3.9056159296787105]), energy=-1074.8451830762128, volume=59.575624050752516), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '514079e9-d6ae-4369-8b80-bf8ca7860540': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[3.9766426435887574, 3.9766426435887574, 3.9766426435887574]), energy=-1074.9158947387848, volume=62.88538094246082), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '6e3c5a28-4edb-4244-9e35-0b8733b946c4': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[4.045218924156295, 4.045218924156295, 4.045218924156295]), energy=-1074.936525208987, volume=66.19513783416937), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '980b72a5-fbea-445c-9075-a880322c8261': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[4.111545777030954, 4.111545777030954, 4.111545777030954]), energy=-1074.9194989203452, volume=69.50489472587755), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n 'a44a935f-6bd2-4279-bbcb-53d5ed890a99': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[4.175799058074337, 4.175799058074337, 4.175799058074337]), energy=-1074.8741797823543, volume=72.81465161758611), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '9987ab9f-1ae9-4172-8e31-b2c9920d4791': {1: Response(output=None, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)}}"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{"collapsed":false,"ExecuteTime":{"end_time":"2024-04-04T12:23:49.409448Z","start_time":"2024-04-04T12:23:49.404281Z"},"jupyter":{"outputs_hidden":false}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"collapsed":false,"ExecuteTime":{"end_time":"2024-04-04T12:23:49.414721Z","start_time":"2024-04-04T12:23:49.411543Z"},"jupyter":{"outputs_hidden":false}},"execution_count":null,"outputs":[]}]} +{"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.11.0","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# jobflow","metadata":{}},{"cell_type":"code","source":"import subprocess\nimport os\nfrom pydantic import BaseModel, Field","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:39.230259Z","start_time":"2024-04-04T12:23:39.139154Z"}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:39.812727Z","start_time":"2024-04-04T12:23:39.233298Z"}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"from ase.build import bulk\nfrom ase.io import write","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.064521Z","start_time":"2024-04-04T12:23:39.815771Z"}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"from adis_tools.parsers import parse_pw","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.342218Z","start_time":"2024-04-04T12:23:40.067769Z"}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"def generate_structures(structure, strain_lst): \n structure_lst = []\n for strain in strain_lst:\n structure_strain = structure.copy()\n structure_strain.set_cell(\n structure_strain.cell * strain**(1/3), \n scale_atoms=True\n )\n structure_lst.append(structure_strain)\n return structure_lst","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.349810Z","start_time":"2024-04-04T12:23:40.344659Z"}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"def plot_energy_volume_curve(volume_lst, energy_lst):\n plt.plot(volume_lst, energy_lst)\n plt.xlabel(\"Volume\")\n plt.ylabel(\"Energy\")\n plt.savefig(\"evcurve.png\")","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.354972Z","start_time":"2024-04-04T12:23:40.351547Z"}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"def write_input(input_dict, working_directory=\".\"):\n filename = os.path.join(working_directory, 'input.pwi')\n os.makedirs(working_directory, exist_ok=True)\n write(\n filename=filename, \n images=input_dict[\"structure\"], \n Crystal=True, \n kpts=input_dict[\"kpts\"], \n input_data={\n 'calculation': input_dict[\"calculation\"],\n 'occupations': 'smearing',\n 'degauss': input_dict[\"smearing\"],\n }, \n pseudopotentials=input_dict[\"pseudopotentials\"],\n tstress=True, \n tprnfor=True\n )","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.361062Z","start_time":"2024-04-04T12:23:40.356433Z"}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"def collect_output(working_directory=\".\"):\n output = parse_pw(os.path.join(working_directory, 'pwscf.xml'))\n return {\n \"structure\": output['ase_structure'],\n \"energy\": output[\"energy\"],\n \"volume\": output['ase_structure'].get_volume(),\n }","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:23:40.365984Z","start_time":"2024-04-04T12:23:40.362667Z"}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"from pymatgen.io.core import InputSet, InputGenerator\nfrom pymatgen.io.ase import MSONAtoms\nfrom typing import Any, Optional, Union\nQE_CMD= \"mpirun -np 1 pw.x -in input.pwi > output.pwo\"\ndef run_qe(qe_cmd=QE_CMD):\n subprocess.check_output(qe_cmd, shell=True, universal_newlines=True)\n\nclass QETaskDoc(BaseModel):\n structure: Optional[MSONAtoms] = Field(None, description=\"ASE structure\")\n energy: Optional[float] = Field(None, description=\"DFT energy in eV\")\n volume: Optional[float] = Field(None, description=\"volume in Angstrom^3\")\n \n @classmethod\n def from_directory(cls, working_directory):\n output=collect_output(working_directory=working_directory)\n # structure object needs to be serializable, i.e., we need an additional transformation\n return cls(structure=MSONAtoms(output[\"structure\"]), energy=output[\"energy\"], volume=output[\"volume\"])\n\nclass QEInputSet(InputSet):\n \"\"\"\n Writes an input based on an input_dict\n \"\"\"\n def __init__(self, input_dict):\n self.input_dict = input_dict\n\n def write_input(self, working_directory=\".\"):\n write_input(self.input_dict, working_directory=working_directory)\n\nfrom dataclasses import dataclass, field\n\n\n\n@dataclass\nclass QEInputGenerator(InputGenerator):\n pseudopotentials: dict = field(default_factory=lambda: {\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"})\n kpts: tuple = (3,3,3)\n calculation: str = \"vc-relax\"\n smearing: float = 0.02\n \n\n def get_input_set(self, structure) -> QEInputSet:\n\n input_dict={\"structure\":structure,\n \"pseudopotentials\":self.pseudopotentials, \n \"kpts\": self.kpts,\n \"calculation\": self.calculation,\n \"smearing\": self.smearing,\n }\n return QEInputSet(input_dict=input_dict)\n\n@dataclass\nclass QEInputStaticGenerator(QEInputGenerator):\n calculation: str = \"scf\"\n\n \ndef write_qe_input_set(structure, input_set_generator=QEInputGenerator(), working_directory=\".\"):\n qis = input_set_generator.get_input_set(structure=structure)\n qis.write_input(working_directory=working_directory)\n \n ","metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false},"ExecuteTime":{"end_time":"2024-04-04T12:23:40.992461Z","start_time":"2024-04-04T12:23:40.368632Z"}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"from dataclasses import dataclass, field\nfrom jobflow import job, Maker\n\n\n@dataclass\nclass BaseQEMaker(Maker):\n \"\"\"\n Base QE job maker.\n\n Parameters\n ----------\n name : str\n The job name.\n input_set_generator : .QEInputGenerator\n A generator used to make the input set.\n \"\"\"\n\n name: str = \"base qe job\"\n input_set_generator: QEInputGenerator = field(default_factory=QEInputGenerator)\n\n @job(output_schema=QETaskDoc)\n def make(\n self, structure\n ) -> QETaskDoc:\n \"\"\"\n Run a QE calculation.\n\n Parameters\n ----------\n structure : MSONAtoms|Atoms\n An Atoms or MSONAtoms object.\n \n Returns\n -------\n Output of a QE calculation\n \"\"\"\n # copy previous inputs\n\n # write qe input files\n write_qe_input_set(\n structure=structure, input_set_generator=self.input_set_generator)\n\n # qe\n run_qe()\n\n # parse qe outputs\n task_doc=QETaskDoc.from_directory(\".\")\n \n return task_doc\n\n@dataclass\nclass StaticQEMaker(BaseQEMaker):\n \"\"\"\n Base QE job maker.\n\n Parameters\n ----------\n name : str\n The job name.\n input_set_generator : .QEInputGenerator\n A generator used to make the input set.\n \"\"\"\n\n name: str = \"static qe job\"\n input_set_generator: QEInputGenerator = field(default_factory=QEInputStaticGenerator)\n\n\n\nfrom jobflow import job, Response, Flow, run_locally\n\n@job\ndef get_ev_curve(structure, strain_lst):\n structures=generate_structures(structure,strain_lst=strain_lst)\n jobs = []\n volumes = []\n energies = []\n for istructure in range(len(strain_lst)):\n new_job = StaticQEMaker().make(structures[istructure])\n jobs.append(new_job)\n volumes.append(new_job.output.volume)\n energies.append(new_job.output.energy)\n return Response(replace=Flow(jobs, output={\"energies\": energies, \"volumes\": volumes}))\n \n@job\ndef plot_energy_volume_curve_job(volume_lst, energy_lst):\n plot_energy_volume_curve(volume_lst=volume_lst, energy_lst=energy_lst)\n\nstructure = bulk('Al', a=4.15, cubic=True)\nrelax = BaseQEMaker().make(structure=MSONAtoms(structure))\nev_curve = get_ev_curve(relax.output.structure, strain_lst=np.linspace(0.9, 1.1, 5))\nplot = plot_energy_volume_curve_job(volume_lst=ev_curve.output[\"volumes\"], energy_lst=ev_curve.output[\"energies\"])\njobs = [relax, ev_curve, plot]\nrun_locally(Flow(jobs), create_folders=True)","metadata":{"ExecuteTime":{"end_time":"2024-04-04T12:26:08.108635Z","start_time":"2024-04-04T12:24:48.120617Z"}},"execution_count":10,"outputs":[{"name":"stdout","text":"2024-04-04 15:55:32,815 INFO Started executing jobs locally\n2024-04-04 15:55:32,892 INFO Starting job - base qe job (72ab2547-8a66-4dd0-a95a-b6255a668cd8)\n","output_type":"stream"},{"name":"stderr","text":"[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00139] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:56:37,395 INFO Finished job - base qe job (72ab2547-8a66-4dd0-a95a-b6255a668cd8)\n2024-04-04 15:56:37,396 INFO Starting job - get_ev_curve (87ad6b9b-9f34-463e-b705-1ab7cdcf9aee)\n2024-04-04 15:56:37,404 INFO Finished job - get_ev_curve (87ad6b9b-9f34-463e-b705-1ab7cdcf9aee)\n2024-04-04 15:56:37,406 INFO Starting job - static qe job (b0759785-155b-4e79-9b72-c626067d81e1)\n","output_type":"stream"},{"name":"stderr","text":"[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00149] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:56:45,563 INFO Finished job - static qe job (b0759785-155b-4e79-9b72-c626067d81e1)\n2024-04-04 15:56:45,564 INFO Starting job - static qe job (514079e9-d6ae-4369-8b80-bf8ca7860540)\n","output_type":"stream"},{"name":"stderr","text":"Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00159] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:56:53,979 INFO Finished job - static qe job (514079e9-d6ae-4369-8b80-bf8ca7860540)\n2024-04-04 15:56:53,980 INFO Starting job - static qe job (6e3c5a28-4edb-4244-9e35-0b8733b946c4)\n","output_type":"stream"},{"name":"stderr","text":"[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00169] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:57:03,600 INFO Finished job - static qe job (6e3c5a28-4edb-4244-9e35-0b8733b946c4)\n2024-04-04 15:57:03,600 INFO Starting job - static qe job (980b72a5-fbea-445c-9075-a880322c8261)\n","output_type":"stream"},{"name":"stderr","text":"Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00179] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:57:14,907 INFO Finished job - static qe job (980b72a5-fbea-445c-9075-a880322c8261)\n2024-04-04 15:57:14,908 INFO Starting job - static qe job (a44a935f-6bd2-4279-bbcb-53d5ed890a99)\n","output_type":"stream"},{"name":"stderr","text":"Note: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n[jupyter-jan-2djanssen-2dqua-2dsso-5fpyiron-5fbase-2d4ksucf2d:00189] mca_base_component_repository_open: unable to open mca_btl_openib: librdmacm.so.1: cannot open shared object file: No such file or directory (ignored)\nNote: The following floating-point exceptions are signalling: IEEE_INVALID_FLAG\n","output_type":"stream"},{"name":"stdout","text":"2024-04-04 15:57:27,174 INFO Finished job - static qe job (a44a935f-6bd2-4279-bbcb-53d5ed890a99)\n2024-04-04 15:57:27,174 INFO Starting job - store_inputs (87ad6b9b-9f34-463e-b705-1ab7cdcf9aee, 2)\n2024-04-04 15:57:27,176 INFO Finished job - store_inputs (87ad6b9b-9f34-463e-b705-1ab7cdcf9aee, 2)\n2024-04-04 15:57:27,176 INFO Starting job - plot_energy_volume_curve_job (9987ab9f-1ae9-4172-8e31-b2c9920d4791)\n2024-04-04 15:57:27,256 INFO Finished job - plot_energy_volume_curve_job (9987ab9f-1ae9-4172-8e31-b2c9920d4791)\n2024-04-04 15:57:27,256 INFO Finished executing jobs locally\n","output_type":"stream"},{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"{'72ab2547-8a66-4dd0-a95a-b6255a668cd8': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[4.045218941837687, 4.045218941837687, 4.045218941837687]), energy=-1074.9365272693506, volume=66.1951387021735), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '87ad6b9b-9f34-463e-b705-1ab7cdcf9aee': {1: Response(output=None, detour=None, addition=None, replace=Flow(name='Flow', uuid='0e46d480-7699-4e03-889b-b4ba65fe3d38')\n 1. Job(name='static qe job', uuid='b0759785-155b-4e79-9b72-c626067d81e1')\n 2. Job(name='static qe job', uuid='514079e9-d6ae-4369-8b80-bf8ca7860540')\n 3. Job(name='static qe job', uuid='6e3c5a28-4edb-4244-9e35-0b8733b946c4')\n 4. Job(name='static qe job', uuid='980b72a5-fbea-445c-9075-a880322c8261')\n 5. Job(name='static qe job', uuid='a44a935f-6bd2-4279-bbcb-53d5ed890a99')\n 6. Job(name='store_inputs', uuid='87ad6b9b-9f34-463e-b705-1ab7cdcf9aee'), stored_data=None, stop_children=False, stop_jobflow=False),\n 2: Response(output={'energies': [OutputReference(b0759785-155b-4e79-9b72-c626067d81e1, .energy), OutputReference(514079e9-d6ae-4369-8b80-bf8ca7860540, .energy), OutputReference(6e3c5a28-4edb-4244-9e35-0b8733b946c4, .energy), OutputReference(980b72a5-fbea-445c-9075-a880322c8261, .energy), OutputReference(a44a935f-6bd2-4279-bbcb-53d5ed890a99, .energy)], 'volumes': [OutputReference(b0759785-155b-4e79-9b72-c626067d81e1, .volume), OutputReference(514079e9-d6ae-4369-8b80-bf8ca7860540, .volume), OutputReference(6e3c5a28-4edb-4244-9e35-0b8733b946c4, .volume), OutputReference(980b72a5-fbea-445c-9075-a880322c8261, .volume), OutputReference(a44a935f-6bd2-4279-bbcb-53d5ed890a99, .volume)]}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n 'b0759785-155b-4e79-9b72-c626067d81e1': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[3.9056159296787105, 3.9056159296787105, 3.9056159296787105]), energy=-1074.8451830762128, volume=59.575624050752516), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '514079e9-d6ae-4369-8b80-bf8ca7860540': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[3.9766426435887574, 3.9766426435887574, 3.9766426435887574]), energy=-1074.9158947387848, volume=62.88538094246082), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '6e3c5a28-4edb-4244-9e35-0b8733b946c4': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[4.045218924156295, 4.045218924156295, 4.045218924156295]), energy=-1074.936525208987, volume=66.19513783416937), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '980b72a5-fbea-445c-9075-a880322c8261': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[4.111545777030954, 4.111545777030954, 4.111545777030954]), energy=-1074.9194989203452, volume=69.50489472587755), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n 'a44a935f-6bd2-4279-bbcb-53d5ed890a99': {1: Response(output=QETaskDoc(structure=MSONAtoms(symbols='Al4', pbc=True, cell=[4.175799058074337, 4.175799058074337, 4.175799058074337]), energy=-1074.8741797823543, volume=72.81465161758611), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)},\n '9987ab9f-1ae9-4172-8e31-b2c9920d4791': {1: Response(output=None, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False)}}"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
    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"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{"collapsed":false,"ExecuteTime":{"end_time":"2024-04-04T12:23:49.409448Z","start_time":"2024-04-04T12:23:49.404281Z"},"jupyter":{"outputs_hidden":false}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"collapsed":false,"ExecuteTime":{"end_time":"2024-04-04T12:23:49.414721Z","start_time":"2024-04-04T12:23:49.411543Z"},"jupyter":{"outputs_hidden":false}},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/pyiron_base.ipynb b/pyiron_base.ipynb index 084b25a..e6fbc20 100644 --- a/pyiron_base.ipynb +++ b/pyiron_base.ipynb @@ -1 +1 @@ -{"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.11.8","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os","metadata":{"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np","metadata":{"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"from ase.build import bulk\nfrom ase.io import write","metadata":{"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"from adis_tools.parsers import parse_pw","metadata":{"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"# Functions","metadata":{}},{"cell_type":"code","source":"def generate_structures(structure, strain_lst): \n structure_lst = []\n for strain in strain_lst:\n structure_strain = structure.copy()\n structure_strain.set_cell(\n structure_strain.cell * strain**(1/3), \n scale_atoms=True\n )\n structure_lst.append(structure_strain)\n return structure_lst","metadata":{"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"def plot_energy_volume_curve(volume_lst, energy_lst):\n plt.plot(volume_lst, energy_lst)\n plt.xlabel(\"Volume\")\n plt.ylabel(\"Energy\")\n plt.savefig(\"evcurve.png\")","metadata":{"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"def write_input(input_dict, working_directory=\".\"):\n filename = os.path.join(working_directory, 'input.pwi')\n os.makedirs(working_directory, exist_ok=True)\n write(\n filename=filename, \n images=input_dict[\"structure\"], \n Crystal=True, \n kpts=input_dict[\"kpts\"], \n input_data={\n 'calculation': input_dict[\"calculation\"],\n 'occupations': 'smearing',\n 'degauss': input_dict[\"smearing\"],\n }, \n pseudopotentials=input_dict[\"pseudopotentials\"],\n tstress=True, \n tprnfor=True\n )","metadata":{"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"def collect_output(working_directory=\".\"):\n output = parse_pw(os.path.join(working_directory, 'pwscf.xml'))\n return {\n \"structure\": output['ase_structure'],\n \"energy\": output[\"energy\"],\n \"volume\": output['ase_structure'].get_volume(),\n }","metadata":{"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"def workflow(project, structure, pseudopotentials): \n # Structure optimization \n job_qe_minimize = pr.wrap_executable(\n job_name=\"job_qe_minimize\",\n write_input_funct=write_input,\n collect_output_funct=collect_output,\n input_dict={\n \"structure\": structure, \n \"pseudopotentials\": pseudopotentials, \n \"kpts\": (3, 3, 3),\n \"calculation\": \"vc-relax\",\n \"smearing\": 0.02,\n },\n executable_str=\"mpirun -np 1 pw.x -in input.pwi > output.pwo\",\n execute_job=True,\n )\n\n # Generate Structures\n structure_lst = pr.wrap_python_function(generate_structures)(\n structure=job_qe_minimize.output.structure, \n strain_lst=np.linspace(0.9, 1.1, 5),\n )\n \n # Energy Volume Curve \n energy_lst, volume_lst = [], []\n for i, structure_strain in enumerate(structure_lst):\n job_strain = pr.wrap_executable(\n job_name=\"job_strain_\" + str(i),\n write_input_funct=write_input,\n collect_output_funct=collect_output,\n input_dict={\n \"structure\": structure_strain, \n \"pseudopotentials\": pseudopotentials, \n \"kpts\": (3, 3, 3),\n \"calculation\": \"scf\",\n \"smearing\": 0.02,\n },\n executable_str=\"mpirun -np 1 pw.x -in input.pwi > output.pwo\",\n execute_job=True,\n )\n energy_lst.append(job_strain.output.energy)\n volume_lst.append(job_strain.output.volume)\n \n return {\"volume\": volume_lst, \"energy\": energy_lst}","metadata":{"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":"# Setup","metadata":{}},{"cell_type":"code","source":"from pyiron_base import Project","metadata":{"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"pr = Project(\"test\")\npr.remove_jobs(recursive=True, silently=True)","metadata":{"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"0it [00:00, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ad61406143494ae5a2d4a01b0cb3750d"}},"metadata":{}}]},{"cell_type":"markdown","source":"# Workflow","metadata":{}},{"cell_type":"code","source":"job_workflow = pr.wrap_python_function(workflow)\njob_workflow.input.project = pr\njob_workflow.input.structure = bulk('Al', a=4.05, cubic=True)\njob_workflow.input.pseudopotentials = {\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"}\njob_workflow.run()","metadata":{"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"The job workflowdbcdde11bde789bfe23b268a60c426c2 was saved and received the ID: 1\nThe job job_qe_minimize was saved and received the ID: 2\nThe job generate_structuresffd5c51457c48a1cff1923a2d98eb48c was saved and received the ID: 3\nThe job job_strain_0 was saved and received the ID: 4\nThe job job_strain_1 was saved and received the ID: 5\nThe job job_strain_2 was saved and received the ID: 6\nThe job job_strain_3 was saved and received the ID: 7\nThe job job_strain_4 was saved and received the ID: 8\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Result","metadata":{}},{"cell_type":"code","source":"plot_energy_volume_curve(\n volume_lst=job_workflow.output.result[\"volume\"], \n energy_lst=job_workflow.output.result[\"energy\"]\n)","metadata":{"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"
    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The latter is independent of the atomistic scale and addresses the general challenge of coupling simulation codes in reproducible workflows. Simulation codes can be integrated in the `pyiron_base` package by either using existing python bindings or alternatively by writing the input files, executing the simulation code and parsing the output files. The following explanations focus on the `pyiron_base` package as a workflow manager, which constructs simulation workflows by combining `job` objects like building blocks.\n\n## Installation / Setup\nThe `pyiron_base` workflow manager can be installed via the python package index or the conda package manager. While no additional configuration is required to use `pyiron_base` on a workstation, the connection to an high performance computing (HPC) cluster requires some additional configuration. The `.pyiron` configuration file in the users home directory is used to specify the resource directory, which contains the configuration of the queuing system. \n\n## Implementation of a new simulation code\nThe `pyiron_base` workflow manager provides two interfaces to implement new simulation codes or simulation workflows. For simulation codes which already provide a python interface the `wrap_python_function()` function is used to convert any python function into a pyiron job object. In analogy external executables can be wrapped using the `wrap_executable()`. Based on these two functions any executable can be wrapped as `Job` object. By naming the `Job` object the user can easily reload the same calculation at any time. Furthermore, `pyiron_base` internally uses the name to generate a directory for each `Job` object to simplify locating the input and output of a given calculation for debugging: ","metadata":{}},{"cell_type":"code","source":"import os\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom ase.io import write\nfrom adis_tools.parsers import parse_pw","metadata":{"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"def write_input(input_dict, working_directory=\".\"):\n filename = os.path.join(working_directory, 'input.pwi')\n os.makedirs(working_directory, exist_ok=True)\n write(\n filename=filename, \n images=input_dict[\"structure\"], \n Crystal=True, \n kpts=input_dict[\"kpts\"], \n input_data={\n 'calculation': input_dict[\"calculation\"],\n 'occupations': 'smearing',\n 'degauss': input_dict[\"smearing\"],\n }, \n pseudopotentials=input_dict[\"pseudopotentials\"],\n tstress=True, \n tprnfor=True\n )","metadata":{"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"def collect_output(working_directory=\".\"):\n output = parse_pw(os.path.join(working_directory, 'pwscf.xml'))\n return {\n \"structure\": output['ase_structure'],\n \"energy\": output[\"energy\"],\n \"volume\": output['ase_structure'].get_volume(),\n }","metadata":{"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"raw","source":"job_test = pr.wrap_executable(\n job_name=\"job_test\",\n write_input_funct=write_input,\n collect_output_funct=collect_output,\n input_dict={\n \"structure\": structure, \n \"pseudopotentials\": pseudopotentials, \n \"kpts\": (3, 3, 3),\n \"calculation\": \"scf\",\n \"smearing\": 0.02,\n },\n executable_str=\"mpirun -np 1 pw.x -in input.pwi > output.pwo\",\n execute_job=False,\n)","metadata":{}},{"cell_type":"markdown","source":"Finally, multiple simulation can be combined in a simulation protocol. In this case the optimization of an Aluminium lattice structure, the calculation of an energy volume curve and the plotting of the resulting curve. ","metadata":{}},{"cell_type":"code","source":"def generate_structures(structure, strain_lst): \n structure_lst = []\n for strain in strain_lst:\n structure_strain = structure.copy()\n structure_strain.set_cell(\n structure_strain.cell * strain**(1/3), \n scale_atoms=True\n )\n structure_lst.append(structure_strain)\n return structure_lst","metadata":{"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"def workflow(project, structure, pseudopotentials): \n # Structure optimization \n job_qe_minimize = pr.wrap_executable(\n job_name=\"job_qe_minimize\",\n write_input_funct=write_input,\n collect_output_funct=collect_output,\n input_dict={\n \"structure\": structure, \n \"pseudopotentials\": pseudopotentials, \n \"kpts\": (3, 3, 3),\n \"calculation\": \"vc-relax\",\n \"smearing\": 0.02,\n },\n executable_str=\"mpirun -np 1 pw.x -in input.pwi > output.pwo\",\n execute_job=True,\n )\n\n # Generate Structures\n structure_lst = pr.wrap_python_function(generate_structures)(\n structure=job_qe_minimize.output.structure, \n strain_lst=np.linspace(0.9, 1.1, 5),\n )\n \n # Energy Volume Curve \n energy_lst, volume_lst = [], []\n for i, structure_strain in enumerate(structure_lst):\n job_strain = pr.wrap_executable(\n job_name=\"job_strain_\" + str(i),\n write_input_funct=write_input,\n collect_output_funct=collect_output,\n input_dict={\n \"structure\": structure_strain, \n \"pseudopotentials\": pseudopotentials, \n \"kpts\": (3, 3, 3),\n \"calculation\": \"scf\",\n \"smearing\": 0.02,\n },\n executable_str=\"mpirun -np 1 pw.x -in input.pwi > output.pwo\",\n execute_job=True,\n )\n energy_lst.append(job_strain.output.energy)\n volume_lst.append(job_strain.output.volume)\n \n return {\"volume\": volume_lst, \"energy\": energy_lst}","metadata":{"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":"As the quantum espresso calculations are the computationally expensive steps they are combined in a python function to be submitted to dedicated computing resources. In contrast the creation of the atomistic structure and the plotting of the energy volume curve are executed in the users process.\n\nThe remaining simulation protocol, can be summarized in a few lines. The required modules are imported, a `Project` object is created which represents a folder on the filesystem, the `wrap_python_function()` is used to convert the computationally expensive steps of the workflow into a single `Job` object and the resulting energy volume curve is plotted: ","metadata":{}},{"cell_type":"code","source":"from ase.build import bulk\nfrom pyiron_base import Project","metadata":{"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"pr = Project(\"test\")\njob_workflow = pr.wrap_python_function(workflow)\njob_workflow.input.project = pr\njob_workflow.input.structure = bulk('Al', a=4.05, cubic=True)\njob_workflow.input.pseudopotentials = {\"Al\": \"Al.pbe-n-kjpaw_psl.1.0.0.UPF\"}\njob_workflow.run()","metadata":{"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"The job workflow895ba469e3d888839622dab8177e3746 was saved and received the ID: 1\nThe job job_qe_minimize was saved and received the ID: 2\nThe job generate_structures81144f1592dde5715ec257eb7f425177 was saved and received the ID: 3\nThe job job_strain_0 was saved and received the ID: 4\nThe job job_strain_1 was saved and received the ID: 5\nThe job job_strain_2 was saved and received the ID: 6\nThe job job_strain_3 was saved and received the ID: 7\nThe job job_strain_4 was saved and received the ID: 8\n","output_type":"stream"}]},{"cell_type":"code","source":"def plot_energy_volume_curve(volume_lst, energy_lst):\n plt.plot(volume_lst, energy_lst)\n plt.xlabel(\"Volume\")\n plt.ylabel(\"Energy\")\n plt.savefig(\"evcurve.png\")","metadata":{"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":"This concludes the first version of the simulation workflow, in the following the submission to HPC resources, the different options for data storage and the publication of the workflow are briefly discussed.","metadata":{}},{"cell_type":"code","source":"plot_energy_volume_curve(\n volume_lst=job_workflow.output.result[\"volume\"], \n energy_lst=job_workflow.output.result[\"energy\"]\n)","metadata":{"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"
    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"},"metadata":{}}]},{"cell_type":"markdown","source":"## Submission to an HPC / Check pointing / Error handling\nWhile the local installation of the `pyiron_base` workflow manager requires no additional configuration, the connection to an HPC system is more evolved. The existing examples provided for specific HPC systems can be converted to jinja2 templates, by defining variables with double curly brackets. A minimalist template could be: \n```\n#!/bin/bash\n#SBATCH --job-name={{job_name}}\n#SBATCH --chdir={{working_directory}}\n#SBATCH --cpus-per-task={{cores}}\n\n{{command}}\n```\nHere the `job_name`, the `working_directory` and the number of compute `cores` can be specified as parameters. In the `pyiron_base` workflow manager such a submission script can then be selected based on its name as parameter of the `server` object:\n```python\njob_workflow.server.queue = \"my_queue\"\njob_workflow.server.cores = 64\n```\nThese lines are inserted before calling the `run()` function. The rest of the simulation protocol remains the same.\n\nWhen simulation protocols are up-scaling and iterated over a large number of parameters, certain parameter combinations might lead to poor conversion or even cause simulation code crashes. In the `pyiron_base` workflow manager these calculation are marked as `aborted`. This gives the user to inspect the calculation and in case the crash was not related to the parameter combination, individual jobs can be removed with the `remove_job()` function. Afterwards, the simulation protocol can be executed again. In this case the `pyiron_base` workflow manager recognizes the already completed calculation and only re-evaluates the removed broken calculation. \n\n## Data Storage / Data Sharing\nIn the `pyiron_base` workflow manager the input of the calculation as well as the output are stored in the hierachical data format (HDF). In addition, `pyiron_base` can use a Structured Query Language (SQL) database, which acts as an index of all the `Job` objects and their HDF5 files. This file-based approach allows the user easily to browse through the results and at the same time the compressed storage in HDF5 and the internal hierarchy of the data format, enable the efficient storage of large tensors, like atomistic trajectories. \n\n## Publication of the workflow\nThe `pyiron_base` workflow manager provides a publication template to publish simulation workflows on Github. This template enables both the publication of the workflow as well as the publication of the results generated with a given workflow. For reproduciblity this publication template is based on sharing a conda environment file `environment.yml` in combination with the Jupyter notebook containing the simulation protocol and the archived `Job` objects. The Jupyter notebook is then rendered as static website with links to enable interactive execution using Jupyterbook and the mybinder service. As the `pyiron_base` workflow manager reloads existing calculation from the archive, a user of the interactive mybinder environment does not have to recompute the computationally expensive steps and still has the opportunity to interact with the provided workflow and data. ","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file From 8301084559f66abe88893241ab3a80b7ba005376 Mon Sep 17 00:00:00 2001 From: Jan Janssen Date: Tue, 30 Apr 2024 11:55:43 -0600 Subject: [PATCH 5/5] add conclusion --- book/_toc.yml | 1 + book/conclusion.md | 2 ++ 2 files changed, 3 insertions(+) create mode 100644 book/conclusion.md diff --git a/book/_toc.yml b/book/_toc.yml index 2baa69b..df3732b 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -6,3 +6,4 @@ chapters: - file: aiida.ipynb - file: jobflow.ipynb - file: pyiron_base.ipynb +- file: conclusion.md diff --git a/book/conclusion.md b/book/conclusion.md new file mode 100644 index 0000000..8ed6fa9 --- /dev/null +++ b/book/conclusion.md @@ -0,0 +1,2 @@ +# Conclusion +The same workflow was implemented in four different simulation frameworks. On the one hand this simplifies the selection of a simulation framework for users who look for a simulation framework for their own code. On the other hand it also highlights how to develop transferable simulation protocols to improve the portability of a given simulation protocol from one framework to the next.