pymatgen-analysis-diffusion

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pymatgen-analysis-diffusion

Formerly pymatgen-diffusion, this is an add-on to pymatgen for diffusion analysis that is developed by the Materials Virtual Lab. Note that it relies on pymatgen for structural manipulations, file io, and preliminary analyses. This is and will always be, a scientific work in progress. Pls check back regularly for more details.

Documentation available via Github Pages <https://github.com/materialsvirtuallab/pymatgen-analysis-diffusion>_.

Major Update (v2021.3.5)

pymatgen-analysis-diffusion is now released as a namespace package pymatgen-analysis-diffusion on PyPI. It should be imported via pymatgen.analysis.diffusion instead pymatgen_diffusion. To install this package via pip::

pip install pymatgen-analysis-diffusion

Features (non-exhaustive!)

1. Van-Hove analysis
2. Probability density
3. Clustering (e.g., k-means with periodic boundary conditions).
4. Migration path finding and IDPP.

Citing

If you use pymatgen-diffusion in your research, please cite the following work::

Deng, Z.; Zhu, Z.; Chu, I.H.; Ong, S. P. Data-Driven First-Principles
Methods for the Study and Design of Alkali Superionic Conductors,
Chem. Mater., 2016, acs.chemmater.6b02648, doi:10.1021/acs.chemmater.6b02648.

You should also include the following citation for the pymatgen core package given that it forms the basis for most of the analyses::

Shyue Ping Ong, William Davidson Richards, Anubhav Jain, Geoffroy Hautier,
Michael Kocher, Shreyas Cholia, Dan Gunter, Vincent Chevrier, Kristin A.
Persson, Gerbrand Ceder. *Python Materials Genomics (pymatgen) : A Robust,
Open-Source Python Library for Materials Analysis.* Computational
Materials Science, 2013, 68, 314-319. doi:10.1016/j.commatsci.2012.10.028.

In addition, some of the analyses may also have relevant publications that you should cite. Please consult the documentation of each module.

Contributing

We welcome contributions in all forms. If you'd like to contribute, please fork this repository, make changes and send us a pull request!

Acknowledgments

We gratefully acknowledge funding from the following agencies for the development of this code:

1. US National Science Foundation’s Designing Materials to Revolutionize and Engineer our Future (DMREF) program under Grant No. 1436976 for the AIMD analysis package.
2. US Department of Energy, Office of Science, Basic Energy Sciences under Award No. DE-SC0012118 for the NEB analysis package.