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://www.materialsproject.org).

Project description

https://circleci.com/gh/materialsproject/pymatgen.svg?style=shield&circle-token=:circle-token https://ci.appveyor.com/api/projects/status/akdyke5jxg6gps45?svg=true https://anaconda.org/matsci/pymatgen/badges/downloads.svg https://coveralls.io/repos/github/materialsproject/pymatgen/badge.svg?branch=master

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, Structure objects.

  2. Extensive input/output support, including support for VASP (http://cms.mpi.univie.ac.at/vasp/), ABINIT (http://www.abinit.org/), 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 own contributions. These contributions can be in the form of additional tools or modules you develop, or feature requests and bug reports. Please report any bugs and issues at pymatgen’s Github page. If you wish to be notified of pymatgen releases, you may become a member of pymatgen’s Google Groups page.

Why use pymatgen?

There are many materials analysis codes out there, both commerical and free, but pymatgen offer several advantages:

  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 CircleCI for continuous integration, which ensures that every commit passes a comprehensive suite of unittests.

  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 have a policy of attributing any code you contribute to any publication you choose. 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. This means that coordinate manipulations are extremely fast and are in fact comparable to codes written in other languages. 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. The plan is to make sure pymatgen will stand the test of time and be the de facto analysis code for most materials and structural analysis.

With effect from version 3.0, pymatgen now supports both Python 2.7 as well as Python 3.x. All developers must ensure that their code passes the unittests on both Py2.7 and 3.x.

Getting pymatgen

Before installing pymatgen, you may need to first install a few critical dependencies manually. Please refer to the official pymatgen page for installation details and requirements, including instructions for the bleeding edge developmental version. For people who are absolutely new to Python packages, it is highly recommended you do the installation using conda, which will make things a lot easier, especially on Windows. Visit materials.sh for instructions on how to use the matsci channel to install pymatgen and other packages.

The version at the Python Package Index (PyPI) is always the latest stable release that is relatively bug-free. The easiest way to install pymatgen on any system is to use pip:

pip install pymatgen

Wheels for Mac (Python 2.7 and 3.5) and Windows (Python 3.5) have been built for convenience.

Some extra functionality (e.g., generation of POTCARs) do require additional setup (please see the pymatgen page).

Change Log

The latest change log is available here.

Using pymatgen

Please refer to the official pymatgen page 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 pymatgen’s documentation 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 of the Materials Virtual 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.

Project details


Release history Release notifications | RSS feed

This version

4.6.1

Download files

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

Source Distribution

pymatgen-4.6.1.tar.gz (1.5 MB view details)

Uploaded Source

Built Distributions

pymatgen-4.6.1-cp36-cp36m-macosx_10_7_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.6m macOS 10.7+ x86-64

pymatgen-4.6.1-cp35-cp35m-win_amd64.whl (1.8 MB view details)

Uploaded CPython 3.5m Windows x86-64

pymatgen-4.6.1-cp35-cp35m-macosx_10_9_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.5m macOS 10.9+ x86-64

pymatgen-4.6.1-cp27-cp27m-macosx_10_6_x86_64.whl (1.8 MB view details)

Uploaded CPython 2.7m macOS 10.6+ x86-64

File details

Details for the file pymatgen-4.6.1.tar.gz.

File metadata

  • Download URL: pymatgen-4.6.1.tar.gz
  • Upload date:
  • Size: 1.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for pymatgen-4.6.1.tar.gz
Algorithm Hash digest
SHA256 be14fa2ac288e379dc4781962907e7c546f2ae44ca9e94ea822acb71f72cf213
MD5 fcd49c374a36ae5fb66133f664a4b215
BLAKE2b-256 5b7b28ba7a1cdff58ba3a8773005838b29c43571eade71dedaf063d987108b3e

See more details on using hashes here.

File details

Details for the file pymatgen-4.6.1-cp36-cp36m-macosx_10_7_x86_64.whl.

File metadata

File hashes

Hashes for pymatgen-4.6.1-cp36-cp36m-macosx_10_7_x86_64.whl
Algorithm Hash digest
SHA256 b5e09da72021c2ea5a5da7e1e04c07f1d48060237d868c5f5ac1952e0e2b6ef9
MD5 eeacafc578b6d9b4b2b86141f2cdf16d
BLAKE2b-256 19a2681ffe8fccd148ad1f6face5357da60a8a8a77febd76935e069cb922ea21

See more details on using hashes here.

File details

Details for the file pymatgen-4.6.1-cp35-cp35m-win_amd64.whl.

File metadata

File hashes

Hashes for pymatgen-4.6.1-cp35-cp35m-win_amd64.whl
Algorithm Hash digest
SHA256 de3cb517640e3806cb36bfd38fdf8a1c32f00c28fce48241f45cb15e3d765d8e
MD5 8c6f336bd4d24cb1b33cd67e47049077
BLAKE2b-256 ad18d48d477ce2ab0e0363074489ae3298d8a9aa856046e182655db52a9d1017

See more details on using hashes here.

File details

Details for the file pymatgen-4.6.1-cp35-cp35m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pymatgen-4.6.1-cp35-cp35m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 c1f0e16aa6210a4635bc669a774d68baca51a277ccd8a27eae8daa49897c66ec
MD5 72e4727341baac060d85fe638c69b288
BLAKE2b-256 4c8696bf253045c6decf2f311c5b7fe4ee2fc571063a4a56df492f36777f7a21

See more details on using hashes here.

File details

Details for the file pymatgen-4.6.1-cp27-cp27m-macosx_10_6_x86_64.whl.

File metadata

File hashes

Hashes for pymatgen-4.6.1-cp27-cp27m-macosx_10_6_x86_64.whl
Algorithm Hash digest
SHA256 5ddfb8299b0856aa7ff99487b40112b2f0a1d8c8753ed2be6121238e686f39d1
MD5 e2e6c8e6bbb2a4e557ae968360578be2
BLAKE2b-256 c6aa12ea7300290b3d6efd62a19d379fe1d93ffd197ac5cd4cf47155ad6cddfe

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