With effect from v2022.0.3, pymatgen, pymatgen.analysis, pymatgen.ext and are now namespace packages. You may refer to the contributing page. 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.

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.
  • 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.

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