Skip to main content

Python Materials Genomics is a robust materials analysis code that defines core object representations for structures and molecules with support for many electronic structure codes. It is currently the core analysis code powering the Materials Project (https://materialsproject.org).

Project description

Logo

CI Status codecov PyPI Downloads Conda Downloads Requires Python 3.9+ Paper

Pymatgen (Python Materials Genomics) is a robust, open-source Python library for materials analysis. These are some of the main features:

  1. Highly flexible classes for the representation of Element, Site, Molecule and Structure objects.
  2. Extensive input/output support, including support for VASP, ABINIT, CIF, Gaussian, XYZ, and many other file formats.
  3. Powerful analysis tools, including generation of phase diagrams, Pourbaix diagrams, diffusion analyses, reactions, etc.
  4. Electronic structure analyses, such as density of states and band structure.
  5. Integration with the Materials Project REST API.

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?

  1. 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.
  2. It is well documented. A fairly comprehensive documentation has been written to help you get to grips with it quickly.
  3. 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.
  4. 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.
  5. 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 Materials Virtual Lab, the ABINIT group and many other research groups.
  6. 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.

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

If you'd like to use the latest unreleased changes on the main branch, you can install directly from GitHub:

pip install -U git+https://github.com/materialsproject/pymatgen

The minimum Python version is 3.9. Some extra functionality (e.g., generation of POTCARs) does require additional setup (see the pymatgen docs).

Change Log

See GitHub releases, docs/CHANGES.md or commit history in increasing order of details.

Using pymatgen

Please refer to the official pymatgen docs for tutorials and examples.

How to cite pymatgen

If you use pymatgen in your research, please consider citing the following work:

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.

Soliciting contributions to 2nd pymatgen paper

If you are a long-standing pymatgen contributor and would like to be involved in working on an updated pymatgen publication, please fill out this co-author registration form or contact @shyuep, @mkhorton and @janosh with questions.

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 Materials Virtual Lab started Pymatgen in 2011 and is still the project lead. Janosh Riebesell (@janosh) and Matthew Horton (@mkhorton) are co-maintainers.

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.

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distribution

pymatgen-2024.2.23.tar.gz (7.2 MB view details)

Uploaded Source

Built Distributions

pymatgen-2024.2.23-cp311-cp311-win_amd64.whl (7.7 MB view details)

Uploaded CPython 3.11 Windows x86-64

pymatgen-2024.2.23-cp311-cp311-win32.whl (7.7 MB view details)

Uploaded CPython 3.11 Windows x86

pymatgen-2024.2.23-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.1 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

pymatgen-2024.2.23-cp311-cp311-macosx_11_0_arm64.whl (7.7 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

pymatgen-2024.2.23-cp310-cp310-win_amd64.whl (7.7 MB view details)

Uploaded CPython 3.10 Windows x86-64

pymatgen-2024.2.23-cp310-cp310-win32.whl (7.7 MB view details)

Uploaded CPython 3.10 Windows x86

pymatgen-2024.2.23-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.0 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

pymatgen-2024.2.23-cp310-cp310-macosx_11_0_arm64.whl (7.7 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

pymatgen-2024.2.23-cp39-cp39-win_amd64.whl (7.7 MB view details)

Uploaded CPython 3.9 Windows x86-64

pymatgen-2024.2.23-cp39-cp39-win32.whl (7.7 MB view details)

Uploaded CPython 3.9 Windows x86

pymatgen-2024.2.23-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.0 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

pymatgen-2024.2.23-cp39-cp39-macosx_11_0_arm64.whl (7.7 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

File details

Details for the file pymatgen-2024.2.23.tar.gz.

File metadata

  • Download URL: pymatgen-2024.2.23.tar.gz
  • Upload date:
  • Size: 7.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/4.0.2 CPython/3.11.8

File hashes

Hashes for pymatgen-2024.2.23.tar.gz
Algorithm Hash digest
SHA256 48764e1bdf8d6b0209b650a96c86947b73d41bc7d2cc4ff13b2ac54dd1c308eb
MD5 f892a772dbed6b84d33b86021277e272
BLAKE2b-256 4da0903a79eeddd76e1913af8d7911dc7edc4b6898cc616642a8229124a1d21a

See more details on using hashes here.

File details

Details for the file pymatgen-2024.2.23-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.2.23-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 d3b2c3fb09fae67d7dc7691293f818b933b639776d3ec50e3c01de16a39a5ed4
MD5 4c7f9def4655c29fc5da011318b92d8e
BLAKE2b-256 a8757424ce76f9c97da99f37008abd1b1b9cd13fc12e979b131c01b1209b912c

See more details on using hashes here.

File details

Details for the file pymatgen-2024.2.23-cp311-cp311-win32.whl.

File metadata

File hashes

Hashes for pymatgen-2024.2.23-cp311-cp311-win32.whl
Algorithm Hash digest
SHA256 59b54b7ee1823c1bc5feaf1ad707ea5044b586216565b6e98f639aa6b515ae6a
MD5 0e813d6f2fd139431a566e3efaa4dbe2
BLAKE2b-256 7c5d069e33b9d4fe539d14a624a667bcd9739245abc9ff6b5d21dc3043e1d10d

See more details on using hashes here.

File details

Details for the file pymatgen-2024.2.23-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.2.23-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 90e162398de7825f44b9b2136234ab8fc3778c663e6c48662dd2db824ba74fb3
MD5 3da4024b72512b2a3e74ad316ab1d78b
BLAKE2b-256 0584cfd8fe5822eedca2f5b0f45dc849aa9f6104f390354b556e82fd6960de7b

See more details on using hashes here.

File details

Details for the file pymatgen-2024.2.23-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.2.23-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 bbdb42c4cee63952595c2150261dc83d8e0635f1f77466b931949ec7bf9fa530
MD5 54d59c6fd78c83b15025a1625dade178
BLAKE2b-256 93c3efba4b6b47f50f44dc8d317f6a77ed1c990bacfd5607e08e277c4ee41f1e

See more details on using hashes here.

File details

Details for the file pymatgen-2024.2.23-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.2.23-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 0073d9b7745009d82cdbc19068ea9f497dc9951cccb5a4fd10e8f7f7eeaeffa4
MD5 e3cf75b848ea9ecdc18186aea4aa0129
BLAKE2b-256 2f1700360a48923bb6b888cf02f24bf6b2225ab56d7b75e48c72743310e0f398

See more details on using hashes here.

File details

Details for the file pymatgen-2024.2.23-cp310-cp310-win32.whl.

File metadata

File hashes

Hashes for pymatgen-2024.2.23-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 c682e86eced5f195fa33810135542b1d2b670e774ff08c42a29ecf5591fc27cb
MD5 1e73066c9389cbdb9ce46f219418a5a0
BLAKE2b-256 6d903cc2e199fd1b1804acdf8a70d6ef4bd4e3a0a296c2685d3791ec88b87fed

See more details on using hashes here.

File details

Details for the file pymatgen-2024.2.23-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.2.23-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4d9be26a1d89aedd81687deee13d65c0b51fed2dd9da1d495c7835820c9f7f75
MD5 e6d53e1864a193730e8be762ce85ca7b
BLAKE2b-256 3107faa685a6971d3686d989a244901247e1408a945cdbec28c43f1cf370204c

See more details on using hashes here.

File details

Details for the file pymatgen-2024.2.23-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.2.23-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 bbe11280e6fbd4309e1d82c72a6bf0ea15fa2399969a1ccb30150fd3070bd218
MD5 67527fbfc5e9fe01f91878d9332db3ab
BLAKE2b-256 81f49f9991dbf9f165861a3be695b4290e8544f5fa71cab454247ed59f22e0ea

See more details on using hashes here.

File details

Details for the file pymatgen-2024.2.23-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.2.23-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 be2a0914cd683a2e89993cbd8d6909a40f0e71d65ecc271c43bd21ddf4f753ba
MD5 80a86c8c17264e226b5b9e409a035653
BLAKE2b-256 0c0012635e71c87042dd66578122efd6c663dcdb76633f4b43e96a5066333e1d

See more details on using hashes here.

File details

Details for the file pymatgen-2024.2.23-cp39-cp39-win32.whl.

File metadata

  • Download URL: pymatgen-2024.2.23-cp39-cp39-win32.whl
  • Upload date:
  • Size: 7.7 MB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/4.0.2 CPython/3.11.8

File hashes

Hashes for pymatgen-2024.2.23-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 fbb266280b0c25f8da27746d823f52f5779d737aef0d66351907055b41969751
MD5 fe0aeb175ee5d29a3b171f8d42b11a6c
BLAKE2b-256 6e87e41401f5ccdc41ebb898aaeca50aeb7fa944e698f18c53f9076cfc1f1a82

See more details on using hashes here.

File details

Details for the file pymatgen-2024.2.23-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.2.23-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 34703f0438b4dad83753bf981afae9ac04c1ef631ba0d88d1df5446f5e9207a6
MD5 d8b8ff848f8f75a3540d76dd5e85ddf4
BLAKE2b-256 01e2838733ae2976df183b7120acedce9bc2af65506d882668791ed08540deef

See more details on using hashes here.

File details

Details for the file pymatgen-2024.2.23-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.2.23-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 1f8b64ca9b4a3cf56fb84c38f5f4a697931e32b57636943f353e22f3e5536b19
MD5 100784bf82d70d1e4c664f82b6995b73
BLAKE2b-256 c8a11d1c6a9500608c898d576156d822a519203d9b4c8381ca4d03f13df22819

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page