The pyiron based module and Jupyter Notebook Melting allow the fully automated computation of melting points of unary crystals for arbitrary interatomic potentials that are compatible with the molecular dynamics engine LAMMPS. It is based on the interface method where the evolution of the solid and the liquid phase are monitored as function of temperature. The only mandatory input parameters required are the chemical element and the interatomic potential file.
The melting point simulation protocol is continously improved based on the feedback from different users. This repository always includes the latest version, older versions are available as tagged releases.
- Version 1.0 - This version was originally published in Computational Materials Science. It uses ovito for structure analysis and supports bcc, fcc and hcp structures.
- Version 1.1 - Adds support for diamond structures. In addition pyscal is used for structure analysis, python 3.9 support is added as well as support for Mac OS X. On windows it is recommended to use the linux subsystem for windows.
- Version 1.2 - Fix numpy Version to 1.19.5.
- Version 1.3 - Update dependencies to use
pyiron_atomisticsrather thanpyiron
pyiron is an integrated development environment (IDE) for computational materials science. It was used to develop the melting point protocol and is designed for the development of complex simulation protocols in general. If you are already a pyiron user or want to understand the detailed steps of the melting point method we recommend using the melting point protocol with pyiron. If you are only interested in the calculated melting point values, the snakemake approach explained below might be more suitable for you. Both approaches are limited to unix operation systems and have been successfully tested with the linux subsystem for windows.
Please install the following packages:
All packages are available via conda-forge and can be installed with the following command:
conda install -c conda-forge pyiron_atomistics nglview lammps jupyter_client scikit-learn pyscal mscorefonts
For the installation of pyiron and the configuration of Lammps and NGLview within pyiron please refer to the pyiron manual.
You can directly download the Jupyter notebook from this Github repositorry script.ipynb copy it to your pyiron projects folder and execute it there. In line 10 and 11 the input parameters can be modified to select a custom potential and change parameters of the melting point calculation. After the calculation finished successfully it creates an output.json file which contains the final melting point prediction as well as the intermediate results.
You can analyse the melting point calculation using the plot.ipynb notebook.
In contrast to the pyiron approach snakemake drastically reduces the number of input parameters available to the user. Snakemake handles the setup of pyiron and the installation of the dependencies via conda. This method is recommended for high throughput calculation as well as automated validation of interatomic potentials. Still it might also be sufficient for users who just want to calculate the melting point for a given interatomic potential. Both approaches are limited to the linux operation system and have been successfully tested with the linux subsystem for windows.
Start with installing snakemake from conda:
conda install -c bioconda -c conda-forge snakemake=5.30
Copy one of the input.json files from the examples to the root of this directory:
cp examples/bccFe/input.json .
The content of the input.json is:
{
"config": [
"pair_style eam/alloy \n",
"pair_coeff * * Fe-C-Bec07.eam Fe C\n"
],
"filename": "./examples/bccFe/Fe-C-Bec07.eam",
"species": ["Fe", "C"],
"element": "Fe"
}
After copying the input.json it can be executed using:
snakemake --use-conda --cores 1
The parameters defined in the input.json file will overwrite those in the Jupyter notebook. With this approach, there is no need to interfere with all the computational and technical details.
The results are saved in the output.json file and can be analysed with the plot.ipynb notebook.
A single melting point calculation takes 50-100 CPU hours, so it makes a lot of sense to run the code in parallel. While the protocol itself is written in a serial way, the individual Lammps calculation can be executed in parallel. To enable parallel execution inset the option:
"cpu_cores": 8,
Either in line 10 of the jupyter notebook or in the input.json file. When snakemake is used it is not necessary to increase the --cores count in the snakemake command.
If you execute the notebook in pyiron, you can simply specify the queue in line 10 by adding the option:
"queue": <queue_name>,
With <queue_name> the name of the queue the calculation should be submitted to. More details about the queuing system configuration in pyiron is available as part of the pysqa package.
In contrast the snakemake command can be directly included in the submit script you usually use to submit calculation to your cluster. Here is an example submit script for the SLURM queuing system:
#!/bin/sh
#SBATCH --time=00:10:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=8
#SBATCH --job-name="melting_point"
snakemake --use-conda --cores 1
In addition to specifying the number of cores in the submit script it is also necessary to set the cpu_cores option in the input.json file as explained above.
If you use the melting point protocol in your scientific work, please consider citing:
@article{melting-paper,
title = {A fully automated approach to calculate the melting temperature of elemental crystals},
journal = {Computational Materials Science}
volume = {187},
pages = {110065},
year = {2021},
doi = {https://doi.org/10.1016/j.commatsci.2020.110065},
url = {https://www.sciencedirect.com/science/article/pii/S0927025620305565},
author = {Li-Fang Zhu and Jan Janssen and Shoji Ishibashi and Fritz Körmann and Blazej Grabowski and Jörg Neugebauer},
keywords = {Interface method, Melting point, Arbitrary potential, pyiron},
}
@article{pyiron-paper,
title = {pyiron: An integrated development environment for computational materials science},
journal = {Computational Materials Science},
volume = {163},
pages = {24 - 36},
year = {2019},
issn = {0927-0256},
doi = {https://doi.org/10.1016/j.commatsci.2018.07.043},
url = {http://www.sciencedirect.com/science/article/pii/S0927025618304786},
author = {Jan Janssen and Sudarsan Surendralal and Yury Lysogorskiy and Mira Todorova and Tilmann Hickel and Ralf Drautz and Jörg Neugebauer},
keywords = {Modelling workflow, Integrated development environment, Complex simulation protocols},
}
@article{pyscal,
author = {Sarath Menon and Grisell Díaz Leines and Jutta Rogal},
title = {{pyscal: A python module for structural analysis of atomic environments}},
year = 2019,
journal = {Journal of Open Source Software},
doi = {10.21105/joss.01824},
url = {https://doi.org/10.21105/joss.01824},
volume = {4},
number = {43},
page = {1824}
}