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Pymatgen Add-ons and External Tools

Add-ons

With effect from v2022.0.3, pymatgen, pymatgen.analysis, pymatgen.ext and and pymatgen.io are now namespace packages. You may refer to the :doc:`contributing page </contributing>`. for details on how to write such packages. This page serves as a universal resource page to list known pymatgen add-ons.

It should be noted that the pymatgen maintainers provide no guarantees whatsoever on the quality or reliability of any of the add-ons listed here. End users should make their own assessment of the functionality and quality.

Please submit a pull request to update this page when if release a new add-on package.

Add-ons for Analysis

  • pymatgen-analysis-diffusion: Provides modules for diffusion analysis, including path determination for NEB calculations, analysis of MD trajectories (RDF, van Hove, Arrhenius plots, etc.). This package is maintained by the Materials Virtual Lab.
  • pymatgen-analysis-defects: Provides functionality related to defect analysis. This package is maintained by Jimmy-Xuan Shen, and officially supported by the Materials Project.

Add-ons for Input/Output

  • pymatgen-io-fleur: Provides modules for reading and writing files used by the fleur DFT code. This package is maintained by the juDFT team.
  • pymatgen-io-openmm: Provides easy IO for performing molecular dynamics on solutions with OpenMM. This package is maintained by Orion Archer Cohen.

Add-ons for External Services

  • None at present

External Tools

If you would like your own tool to be listed here, please submit a PR! For a more complete but less curated list, have a look at pymatgen dependents.

  • Atomate2: atomate2 is a library of computational materials science workflows.
  • LobsterPy: Automatically analyze Lobster runs <https://cohp.de>_.
  • pymatviz: Complements pymatgen with additional plotting functionality for larger datasets common in materials informatics.
  • DiSCoVeR: A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces.
  • rxn-network: Reaction Network is a Python package for predicting likely inorganic chemical reaction pathways using graph theory.
  • Matbench: Benchmarks for machine learning property prediction.
  • Matbench Discovery: Benchmark for machine learning crystal stability prediction.
  • matgl: Graph deep learning library for materials. Implements M3GNet and MEGNet in DGL and Pytorch with more to come.
  • chgnet: Pretrained universal neural network potential for charge-informed atomistic modeling.