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).
Official docs: `http://pymatgen.org <http://pymatgen.org/>`_
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,
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
<https://github.com/materialsproject/pymatgen>`_. 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 <https://circleci.com>`_ and `Appveyor <https://www.appveyor.com/>`_
for continuous integration on the Linux and Windows platforms,
respectively, which ensures that every commit passes a comprehensive suite
of unittests. The coverage of the unittests can be seen at
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 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.
With effect from version 3.0, pymatgen now supports both Python 2.7 as well
as Python 3.x.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|File Name & Checksum SHA256 Checksum Help||Version||File Type||Upload Date|
|pymatgen-2017.12.16-cp27-cp27m-macosx_10_7_x86_64.whl (2.0 MB) Copy SHA256 Checksum SHA256||cp27||Wheel||Dec 15, 2017|
|pymatgen-2017.12.16-cp35-cp35m-macosx_10_9_x86_64.whl (2.0 MB) Copy SHA256 Checksum SHA256||3.5||Wheel||Dec 15, 2017|
|pymatgen-2017.12.16-cp36-cp36m-macosx_10_9_x86_64.whl (2.0 MB) Copy SHA256 Checksum SHA256||cp36||Wheel||Dec 15, 2017|
|pymatgen-2017.12.16.tar.gz (1.7 MB) Copy SHA256 Checksum SHA256||–||Source||Dec 15, 2017|