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://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.

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

Download files

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

Source Distribution

pymatgen-4.4.6.tar.gz (1.4 MB view details)

Uploaded Source

Built Distributions

pymatgen-4.4.6-cp35-cp35m-win_amd64.whl (1.7 MB view details)

Uploaded CPython 3.5m Windows x86-64

pymatgen-4.4.6-cp35-cp35m-macosx_10_6_x86_64.whl (1.8 MB view details)

Uploaded CPython 3.5m macOS 10.6+ x86-64

pymatgen-4.4.6-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-4.4.6.tar.gz.

File metadata

  • Download URL: pymatgen-4.4.6.tar.gz
  • Upload date:
  • Size: 1.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for pymatgen-4.4.6.tar.gz
Algorithm Hash digest
SHA256 7eb03924222be018c09ebb11743975a244ce2b600c3b40ab6dbcc028e0c24e7a
MD5 23e3ebdc43ab5e26882d395c7497c882
BLAKE2b-256 ae229103e286dcb00876e2f0e24e428a5ddcb0ac0f9ba45e1841c7a69432cc07

See more details on using hashes here.

File details

Details for the file pymatgen-4.4.6-cp35-cp35m-win_amd64.whl.

File metadata

File hashes

Hashes for pymatgen-4.4.6-cp35-cp35m-win_amd64.whl
Algorithm Hash digest
SHA256 b4b56a59cc71b0a80b5b8c53fcfb0233ef55ac5c44e0f91526d513f824d25083
MD5 2eaa575282ece14ae65c56d4b20780c3
BLAKE2b-256 2afb051b7eacd84462fb2ff2ad7a2ee18a60ebf58a96703aa60bc641135c6afb

See more details on using hashes here.

File details

Details for the file pymatgen-4.4.6-cp35-cp35m-macosx_10_6_x86_64.whl.

File metadata

File hashes

Hashes for pymatgen-4.4.6-cp35-cp35m-macosx_10_6_x86_64.whl
Algorithm Hash digest
SHA256 5867f340b0299b1a53e70ce440fb0133139c7522b97b1f99bc2786ccdac8622b
MD5 45779d83f0979aa69cb85398d59befeb
BLAKE2b-256 8816276b554fdbfc043c397058c9f5c2b9a1833dfd2e4f97b38258576e43f199

See more details on using hashes here.

File details

Details for the file pymatgen-4.4.6-cp27-cp27m-macosx_10_6_x86_64.whl.

File metadata

File hashes

Hashes for pymatgen-4.4.6-cp27-cp27m-macosx_10_6_x86_64.whl
Algorithm Hash digest
SHA256 cf824d638f2f5b6708e441ea0ae922d6f185e8cfeb444449224da5f1390d9b83
MD5 a9dc30cd2015c84478393c9ec04de2c4
BLAKE2b-256 b1cb4d55d469da98f983a008df783f018d6df6df03a8f48acff2019bedaa30e2

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