Skip to main content

Add-on to pymatgen for diffusion analysis.

Project description

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. In particular, pymatgen’s DiffusionAnalyzer is used heavily. The purpose of this add-on is to provide other diffusion analyses, using trajectories extracted using the DiffusionAnalyzer class.

This is, and will always be, a scientific work in progress. Pls check back for more details.

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 addtion, 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!

Acknowledgements

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
pymatgen_diffusion-2018.11.17-py2.py3-none-any.whl (34.1 kB) Copy SHA256 hash SHA256 Wheel py2.py3
pymatgen-diffusion-2018.11.17.tar.gz (25.2 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page