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---
layout: default
title: Add-ons
nav_order: 7
---
# Add-ons
With effect from v2022.0.3, pymatgen, pymatgen.analysis, pymatgen.ext and pymatgen.io are now [namespace packages](http://packaging.python.org/guides/packaging-namespace-packages). You may refer to the [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](http://pypi.org/project/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](https://pypi.org/project/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](http://pypi.org/project/pymatgen-io-fleur): Provides modules for reading and writing files used by the [`fleur`](https://www.flapw.de/rel) DFT code. This package is maintained by the juDFT team.
* [pymatgen-io-openmm](https://github.com/orionarcher/pymatgen-io-openmm): Provides easy IO for performing molecular dynamics on solutions with OpenMM. This package is maintained by Orion Archer Cohen.
* [pymatgen-io-espresso](https://github.com/Griffin-Group/pymatgen-io-espresso): Provides support for the [Quantum ESPRESSO (QE)](https://www.quantum-espresso.org) DFT code. This package was created by [Omar Ashour](https://github.com/oashour) and maintained in the [Griffin Group](https://sineadgriffin.com) repository.
* [pymatgen-io-validation](https://github.com/materialsproject/pymatgen-io-validation/): Provides comprehensive validation of VASP calculations. This package was created by [Matthew Kuner](https://github.com/matthewkuner) and maintained in the Materials Project repository.
* [pymatgen-io-aims](https://gitlab.com/FHI-aims-club/pymatgen-io-aims): Provides support for the [FHI-aims](https://fhi-aims.org/) DFT code. This package is created and maintained by Thomas A. R. Purcell, Andrey Sobol, and the MS1P team.
## 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](https://github.com/materialsproject/pymatgen/network/dependents).
* [Atomate2](https://github.com/materialsproject/atomate2): atomate2 is a library of computational materials science workflows.
* [LobsterPy](https://github.com/JaGeo/LobsterPy): Automatically analyze [Lobster runs](https://cohp.de).
* [pymatviz](https://github.com/janosh/pymatviz): Complements `pymatgen` with additional plotting functionality for larger datasets common in materials informatics.
* [DiSCoVeR](https://github.com/sparks-baird/mat_discover): A materials discovery algorithm geared towards exploring
high-performance candidates in new chemical spaces.
* [rxn-network](https://github.com/GENESIS-EFRC/reaction-network): Reaction Network is a Python package for predicting likely inorganic chemical reaction pathways using graph theory.
* [Matbench](https://github.com/materialsproject/matbench): Benchmarks for machine learning property prediction.
* [Matbench Discovery](https://github.com/janosh/matbench-discovery): Benchmark for machine learning crystal stability prediction.
* [matgl](https://github.com/materialsvirtuallab/matgl): Graph deep learning library for materials. Implements M3GNet and MEGNet in DGL and Pytorch with more to come.
* [chgnet](https://github.com/CederGroupHub/chgnet): Pretrained universal neural network potential for charge-informed atomistic modeling.
* [DebyeCalculator](https://github.com/FrederikLizakJohansen/DebyeCalculator): A vectorised implementation of the Debye Scattering Equation on CPU and GPU.
* [ramannoodle](https://github.com/wolearyc/ramannoodle): Efficiently compute off-resonance Raman spectra from first principles calculations (e.g. VASP) using polynomial and ML models.
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