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

Documentation available viaGithub Pages.

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

pip install pymatgen-analysis-diffusion

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

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

Deng, Z.; Zhu, Z.; Chu, I.H.; Ong, S. P. Data-Driven First-PrinciplesMethods 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 packagegiven 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.* ComputationalMaterials Science, 2013, 68, 314-319. doi:10.1016/j.commatsci.2012.10.028.

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

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

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

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