diff --git a/jobflow.ipynb b/jobflow.ipynb
index 7778775..dc7580b 100644
--- a/jobflow.ipynb
+++ b/jobflow.ipynb
@@ -1 +1,374 @@
-{"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":"<Figure size 640x480 with 1 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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
+{
+ "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": {}
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Jobflow allows you to build workflows that are not necessarily related to material science. However, it was developed in the context of the Materials Project and therefore forms the basis for atomate2, a software package for workflows related to the data generation of the Materials Project. It was especially build to simplify the definition of dynamic workflows - those that create jobs during run time."
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Installation / Setup\n",
+    "Jobflow itself can be installed via ‘pip install‘ and directly run with the default setup. Jobflow will then internally rely on a predefined database which is currently a memory database as defined in the software package maggma. For large-scale usage, a MongoDB-like database might be specified for each project in a jobflow.yaml file.\n",
+    "To run jobflow in a high-throughput setup (i.e., parallel execution of independent parts in the workflow), additional packages are required. Currently, fireworks (https://materialsproject.github.io/fireworks/) and jobflow-remote (https://matgenix.github.io/jobflow-remote/) exist. They both require a MongoDB database.\n",
+    "In the case of FireWorks, however, a MongoDB database needs to be connected to the compute nodes. Jobflow-remote allows remote submission options that only require a MongoDB database on the submitting computer but not the compute nodes. It can also deal with two-factor authentification."
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Basic introduction to Jobflow concepts\n",
+    "Before starting with implementing a new simulation core, we need to discuss the core concepts of jobflow. jobflow has been written in Python. Jobflow defines Job and Flow objects. Jobs are classes that allow for the delayed execution of a function. Functions can be easily transformed into a Job with the @job decorator, as demonstrated here with a test function:"
+   ]
+  },
+  {
+   "metadata": {
+    "jupyter": {
+     "is_executing": true
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from jobflow import job\n",
+    "@job\n",
+    "def my_function(my_parameter: int):\n",
+    "    return my_parameter\n",
+    "\n",
+    "job1 = my_function(my_parameter=1)\n"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "To build workflows, the Flow object is used. Here, Jobs are connected via their outputs so that it is possible to derive the order of the Jobs as run time. To build the workflow, only the list of connected Jobs has to be used to initialize a flow.\n"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "from jobflow import job, Flow\n",
+    "@job\n",
+    "def my_function(my_parameter: int):\n",
+    "    return my_parameter\n",
+    "@job\n",
+    "def my_second_function(my_parameter: int):\n",
+    "    return my_parameter\n",
+    "\n",
+    "job1 = my_function(my_parameter=1)\n",
+    "job2 = my_second_function(job1.output)\n",
+    "flow = Flow([job1, job2], job2.output)"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "To simplify the implementation of Jobs and Flow, the Maker object has been introduced. It is an extended dataclass object that generates a Job or Flow with the make method. Dataclasses are used as they allow to define immutable default values. These Makers can then be used to reuse code via usual inheritance in Python.\n"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": [
+    "from dataclasses import dataclass\n",
+    "from jobflow import Maker\n",
+    "@dataclass\n",
+    "class MyMaker(Maker):\n",
+    "    name: str = \"My Maker\"\n",
+    "    scaling: float = 1.1\n",
+    "\n",
+    "@job\n",
+    "def make(self, my_parameter: int):\n",
+    "    return my_parameter * self.scaling\n",
+    "\n",
+    "job1=MyMaker().make(my_parameter=1)"
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "Instead of returning a Job or Flow object, also a Response object can be returned, which allows the definition of dynamic workflows where the number of additional jobs is unclear before execution. There are different options how exactly the Response object will insert the new list of jobs.\n",
+    "We will now use this knowledge to implement the Quantum Espresso-related tasks. It’s important to point out that this is only a basic implementation, and further extensions towards data validation or for nicer user experience can be added. "
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "One additional point needs to be mentioned: all outputs of a job need to be transformed into a json-like format. Therefore, we use a pymatgen Structure object here instead of an ase Atoms object. Structure inherits from the Python package monty’s MSONable class allowing an easy serialization into a dict. In addition, commands to execute jobs might be set in such a way that users can easily adapt them in a configuration file.\n"
+  },
+  {
+   "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": "<Figure size 640x480 with 1 Axes>",
+      "image/png": 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"
+     },
+     "metadata": {}
+    }
+   ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "## Submission to an HPC / Check pointing / Error handling\n",
+    "* jobflow: has a command to run jobs in serial with a simple job script\n",
+    "* fireworks and jobflow-remote exist to simplify submission of jobs, they allow for checkpointing\n",
+    "* error handling can be implemented but is not directly handled at the jobflow level, currently in atomate2, we rely on custodian\n",
+    "## Data Storage / Data Sharing\n",
+    "Key data can be saved in a dedicated MongoDB-like database. This data can be validated with the help of\n",
+    "pydantic. The latter also allows a direct connection to Fast-API. The Materials Project data generation\n",
+    "relies on similar features. Direct calculation outputs will be saved on the computer where the job has been\n",
+    "run. There are no dedicated mechanisms to save whole projects. The database can, however, be used to\n",
+    "accomplish this.\n",
+    "## Publication of the workflow\n",
+    "The jobflow infrastructure currently does not provide a dedicated platform for publishing a workflow. How\u0002ever, workflows related to computational materials science have been collected in the pacakge atomate2. In\n",
+    "addition, users can build their own package by relying on jobflow and share it as any Python project."
+   ]
+  },
+  {
+   "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": []
+  }
+ ]
+}