Skip to content

yuzie007/clustergb

 
 

Repository files navigation

Cluster GB

Introduction

ClusterGB is a set of Python scripts designed to facilitate easy calculations of planar grain boundaries (GBs) using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). In order to accommodate even low-symmetry boundaries, ClusterGB uses vacuum clusters to eliminate the need for periodic boundary conditions.

The user first begins a Project to specify a material (i.e. an emperical potential), then inside this Project creates a series of Jobs--one for each GB to be studied.

Initializing a new Job generates a GB structure file with the requested GB character and (stochastically) minimized microscopic degrees of freedom. From here various cgb_run_* scripts can be called from either the Job or Project level of the hierarchy to calculate properties of a particular GB or all the GBs in the project, respectively. An important example is calculating

ClusterGB can accommodate any macroscopic GB character, but currently only works for single-species host structures with FCC or BCC crystal structure, and relies on LAMMPS_ with Embedded Atom Method potentials with "alloy" or "Finnis-Sinclair" formatting.

For more details, please read the documentation accompanying the source code.

Installation

ClusterGB has only been tested on Unix systems. Sorry, Windows.

0) Environment

ClusterGB was written for Python2.7 and uses a variety of Python packages. If you are already familiar with Python, simply ensure that your environment has all of the packages listed below. If you are unfamiliar with Python, you can download it for free from a variety of places, but I recommend an Anaconda distribution. With Anaconda, any missing packages can be easily installed with conda install ${PACKAGE_NAME}

All required Packages (most scientific installations of Python will already have these):

  • argparse
  • logging
  • matplotlib (just for cgb_interactive.py)
  • numpy
  • os
  • readline (just for cgb_interactive.py)
  • rlcompleter (just for cgb_interactive.py)
  • scipy
  • seaborn (just for cgb_interactive.py)
  • shutil
  • sklearn (just for the example project)
  • subprocess
  • time
  • yaml

The heavy-lifting of calculating energies and forces for ClusterGB is done using LAMMPS. LAMMPS is invoked as a separate subprocess, so you just need a regular LAMMPS executable, which can be built from the source code or, for some systems, downloaded pre-built here. Most versions of LAMMPS should be fine, but ClusterGB was tested using the 1 Jul 2016 release.

If MPI runs on your machine, it can be used to accelerate the LAMMPS calculations by running them in parallel.

Finally, Voronoi analysis (optional) is performed by calling Voro++, which is also free to download.

1) Download

If you have these docs, you probably already have the source code for ClusterGB. If not, you can download it from Github. Python is an interpreted language, so no special compilation is required, just add the clustergb folder (containing cgb_init_project.py etc.) to your path for easy use.

2) Configuration

In clustergb/config.yml, set the lammps, (optionally) voro, and (optionally) mpi fields to point to valid executables on your machine for LAMMPS, Voro++, and MPI, respectively.

That's it, you're ready to go with ClusterGB.

Licence

ClusterGB is released under the MIT License.

Citing

Different functionality of clustergb requires different citations. Please consider which pieces of the code you're using and add citations to your journal article/website accordingly.

Main code:

Voronoi analysis:

(Assuming you've got a copy of voro++ that you've linked to and are using cgb's default Voro procedures)

Coordination analysis:

  • Huang, Grabowski, McEniry, Trinkle, and Neugebauer, Phys. Status Solidi B 252 (2015) 1907

Bond orientational order parameters:

  • Steinhardt, Nelson, and Ronchetti, Phys Rev B 28 (1983)

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%