pymatgen-core is the foundational subset of Pymatgen (Python Materials Genomics), the robust open-source Python library for materials analysis. It contains the core data structures, symmetry/lattice operations, and the I/O layer for many DFT/MD codes, with minimal dependencies. The full pymatgen package builds on top of pymatgen-core and adds higher-level analysis modules (phase diagrams, Pourbaix diagrams, diffusion analyses, etc.) and add-ons.
Some of the main features of pymatgen-core:
- Highly flexible classes for the representation of
Element, Site, Molecule and Structure objects.
- Extensive input/output support, including support for VASP, ABINIT, CIF, Gaussian, XYZ, and many other file formats.
- Core analysis tools, including symmetry detection, structure matching, bond valence, Ewald summation, and more.
- Electronic structure data classes for density of states and band structure.
- Foundational utilities used by the broader pymatgen ecosystem (the full
pymatgen package, pymatgen-analysis-* add-ons, atomate, FireWorks, etc.).
Pymatgen is free to use. However, we also welcome your help to improve this library by making your contributions. These contributions can be in the form of additional tools or modules you develop, or feature requests and bug reports. The following are resources for pymatgen:
Why use pymatgen-core?
- It is (fairly) robust. Pymatgen is used by thousands of researchers and is the analysis code powering the
Materials Project. The analysis it produces survives rigorous scrutiny every single day. Bugs tend to be found
and corrected quickly. Pymatgen also uses Github Actions for continuous integration, which ensures that every
new code passes a comprehensive suite of unit tests.
- It is well documented. A fairly comprehensive documentation has been written to help you get to grips with
it quickly.
- It is open. You are free to use and contribute to
pymatgen. It also means that pymatgen is continuously
being improved. We will attribute any code you contribute to any publication you specify. Contributing to
pymatgen means your research becomes more visible, which translates to greater impact.
- It is fast. Many of the core numerical methods in
pymatgen have been optimized by vectorizing in
numpy/scipy. This means that coordinate manipulations are fast. Pymatgen also comes with a complete system
for handling periodic boundary conditions.
- It will be around. Pymatgen is not a pet research project. It is used in the well-established Materials
Project. It is also actively being developed and maintained by the Materialyze Lab, the ABINIT group and
many other research groups.
- A growing ecosystem of developers and add-ons. Pymatgen has contributions from materials scientists all over
the world. We also now have an architecture to support add-ons that expand
pymatgen's functionality even
further. Check out the contributing page and add-ons page for details and examples.
- It is lightweight.
pymatgen-core carries only the dependencies needed for the core data structures and
I/O. Use it directly when you need a small footprint, or install the full pymatgen package when you need the higher-level analysis modules.
Installation
The version at the Python Package Index PyPI is always the latest stable release that is relatively bug-free and can be installed via pip:
pip install pymatgen-core
If you need the higher-level analysis modules (phase diagrams, Pourbaix, reaction calculator, etc.), install the full pymatgen package instead, which depends on pymatgen-core:
pip install pymatgen
If you'd like to use the latest unreleased changes on the main branch, you can install pymatgen-core directly from GitHub:
pip install -U git+https://github.com/materialsproject/pymatgen-core
Note that pymatgen-core imports under the same pymatgen namespace (e.g. from pymatgen.core import Structure), so existing code continues to work. Some extra functionality (e.g., generation of POTCARs) does require additional setup (see the pymatgen docs).
Change Log
See GitHub releases, CHANGES.md or commit history in increasing order of details. Rendered docs (including the merged changelog) live at https://pymatgen.org.
Using pymatgen
Please refer to the official pymatgen docs for tutorials and examples. Dr Anubhav Jain (@computron) has also created
a series of tutorials and YouTube videos, which is a good resource, especially for beginners.
How to cite pymatgen
If you use pymatgen in your research, please cite the following work:
Janine George, Matthew K. Horton, Aaron D. Kaplan, Seán R. Kavanagh, Janosh Riebesell, Andrew S. Rosen,
Haoyu Yang, Felix Adams, Mahdi Amachraa, Oxana Andriuc, Omar A. Ashour, Saurabh Bajaj, Ella Banyas, Luis
Barroso-Luque, Karlo Berket, Noam Bernstein, Arpit Bhardwaj, Ali Hussain Umar Bhatti, Samuel M. Blau,
Brandon Bocklund, Alexander Bonkowski, Danny Broberg, Guillaume Brunin, Kyle Bystrom, Charles A. Cardot,
Rees Chang, Chi Chen, Wei Chen, Xin Chen, Yuan Chiang, Kamal Choudhary, Orion A. Cohen, Miguel D. Costa,
Stephen T. Dacek III, Maarten de Jong, Tom Demeyere, Alan K. Dozier, Alexander Dunn, Shyam Dwaraknath,
Maxwell T. Dylla, Kristjan Eimre, Alexander R. Epstein, Christina Ertural, Marco Esters, Matthew L.
Evans, Johannes Falke, Adam Fekete, Bernard Field, Alexandr Fonari, Nathan C. Frey, Dale W. Gaines II,
Alex M. Ganose, Matteo Giantomassi, Rhys E. A. Goodall, Petre-Flaviu Gostin, Aayush Gupta, Viet-Anh Ha,
Geoffroy Hautier, Niclas Heinsdorf, Michael F. Herbst, Tingzheng Hou, Patrick Huck, Yuji Ikeda, Adam J.
Jackson, Saivenkataraman Jayaraman, Finn R. Kearns, Sunghyun Kim, Ryan S. Kingsbury, Adam M. Krajewski,
Yu Kumagai, Matthew C. Kuner, William T. Laderer, Weitang Li, Xiang-Guo Li, Yu Hsuan Liang, Hanyu Liu, Ke
Liu, Miao Liu, Ahmad Lutfi, Olivier Marchand, Matthew J. McDermott, Bharat K. Medasani, Fanchen Meng,
Kyle D. Miller, Henrique Miranda, Rohith Srinivaas Mohanakrishnan, Joseph H. Montoya, Guy C. Moore,
Benjamin J. Morgan, Jason M. Munro, Koki Muraoka, Aakash A. Naik, Willis O'Leary, Anthony Onwuli, Jaesuk
Park, Drew F. Parsons, Kai Pei, Guido Petretto, Thomas A. R. Purcell, Ji Qi, Xiaohui Qu, Ihor Radchenko,
Nav Nidhi Rajput, Francesco Ricci, Benjamin S. Rich, William D. Richards, Gian-Marco Rignanese, Xiaoxu
Ruan, Germain Salvato Vallverdu, Jonathan Schmidt, Jimmy-Xuan Shen, Ryuichi Shimogawa, Kohei Shinohara,
Arunima K. Singh, Eric Sivonxay, Stef Smeets, Tess E. Smidt, Andrei Sobolev, Evan Walter Clark
Spotte-Smith, Christopher Stihl, Wenhao Sun, Jack D. Sundberg, Yuta Suzuki, Leopold Talirz, Hanmei Tang,
Eric Taw, Cooper Tezak, Félix Therrien, Scott D. Thiel, Steven B. Torrisi, Mark E. Turiansky, Katharina
Ueltzen, Michiel J. van Setten, Josua Vieten, Sudarshan Vijay, Nicholas Wagner, Frank Wan, Amanda Wang,
Jingyang Wang, Zhenbin Wang, Logan Ward, Thomas Warford, David Waroquiers, Mingjian Wen, Oskar Weser,
Nicholas Winner, Donald Winston, Michael Wolloch, Brandon M. Wood, Rachel Woods-Robinson, Yihan Xiao,
Yaoshu Xie, Firat Yalcin, Ruo Xi Yang, Weike Ye, Pezhman Zarabadi-Poor, Jinzhe Zeng, Hongsheng Zhao,
Kaifeng Zheng, He Zhu, Zhuoying Zhu, Nils E. R. Zimmermann, Daniel Zügner, Kristin A. Persson, Anubhav
Jain, Shyue Ping Ong.
pymatgen: 15 years of community growth, new functionality, and future prospects.
arXiv, 2026, arXiv:2610.23538. doi:10.48550/arXiv.2610.23538
Please also consider citing the original pymatgen paper:
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 addition, some of pymatgen's functionality is based on scientific advances/principles developed by the
computational materials scientists in our team. Please refer to the pymatgen docs on how to cite them.
License
Pymatgen is released under the MIT License. The terms of the license are as follows:
The MIT License (MIT) Copyright (c) 2011-2012 MIT & LBNL
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of
the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO
THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
About the Pymatgen Development Team
Shyue Ping Ong (@shyuep) of the Materialyze Lab started Pymatgen in 2011 and is still the project lead.
The pymatgen development team is the set of all contributors to the pymatgen project, including all subprojects.
Our Copyright Policy
Pymatgen uses a shared copyright model. Each contributor maintains copyright over their contributions to pymatgen.
But, it is important to note that these contributions are typically only changes to the repositories. Thus, the
pymatgen source code, in its entirety is not the copyright of any single person or institution. Instead, it is the
collective copyright of the entire pymatgen Development Team. If individual contributors want to maintain a
record of what changes/contributions they have specific copyright on, they should indicate their copyright in the
commit message of the change, when they commit the change to one of the pymatgen repositories.
