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Official docs: http://www.pymatgen.org

Pymatgen (Python Materials Genomics) is a robust, open-source Python library for materials analysis. It currently powers the public Materials Project (https://www.materialsproject.org), an initiative to make calculated properties of all known inorganic materials available to materials researchers. These are some of the main features:

1. Highly flexible classes for the representation of Element, Site, Molecule, Structure objects.
2. Extensive io capabilities to manipulate many VASP (http://cms.mpi.univie.ac.at/vasp/) and ABINIT (http://www.abinit.org/) input and output files and the crystallographic information file format. This includes generating Structure objects from vasp input and output. There is also support for Gaussian input files and XYZ file for molecules.
3. Comprehensive tool to generate and view compositional and grand canonical phase diagrams.
4. Electronic structure analyses (DOS and Bandstructure).
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 even simple things such as 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.

With effect from version 3.0, pymatgen now supports both Python 2.7 as well as Python 3.x. All underlying core dependencies (numpy, pyhull and the spglib library) have been made Python 3 compatible, and a completely rewritten CIF parser module (courtesy of William Davidson Richards) has removed the dependency on PyCIFRW. We will support Python >= 3.3 (ignoring v3.1 and v3.2). With the release of a new major version, we also took the opportunity to streamline and cleanup some of the code, which introduces a few backward incompatibilities.

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 in the Materials Project. As such, the analysis it produces survives rigorous scrutiny every single day. Bugs tend to be found and corrected quickly. Furthermore, pymatgen uses CircleCI for continuous integration, which ensures that all unittests pass with every commit.
2. It is well documented. A fairly comprehensive documentation has been written to help you get to grips with it quickly. That means more efficient research.
3. It is open. That means you are free to use it, and you can also contribute to it. 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 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.

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.

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

pip install pymatgen

or:

easy_install pymatgen

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

Note for Windows users: Given that pymatgen requires several Python C extensions, it is generally recommended that you install it in a cygwin or equivalent environment with the necessary compilers.

The latest change log is available here.

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

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.

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.