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 (www.materialsproject.org).
Pymatgen (Python Materials Genomics) is a robust, open-source Python library for materials analysis. It currently powers the public Materials Project (http://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:
- Highly flexible classes for the representation of Element, Site, Molecule, Structure objects.
- 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.
- Comprehensive tool to generate and view compositional and grand canonical phase diagrams.
- Electronic structure analyses (DOS and Bandstructure).
- Integration with the Materials Project REST API.
Pymatgen, like all scientific research, will always be a work in progress. While the development team will always strive to avoid backward incompatible changes, they are sometimes unavoidable, and tough decisions have to be made for the long term health of the code.
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.
Why use pymatgen?
There are many materials analysis codes out there, both commerical and free. So you might ask - why should I use pymatgen over others? Pymatgen offer several advantages over other codes out there:
- 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.
- 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.
- 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.
- 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.
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.
- Numpy’s distutils is needed to compile the spglib and pyhull dependencies. This should be the first thing you install.
- Pyhull and PyCifRW. The recent versions of pip does not allow the installation of externally hosted files. Furthermore, there are some issues with easy_install for these extensions. Install both these dependencies manually using “pip install <package> –allow-external <package> –allow-unverified <package>”.
The version at the Python Package Index (PyPI) is always the latest stable release that will be hopefully, be relatively bug-free. The easiest way to install pymatgen on any system is to use easy_install or pip, as follows:
pip install pymatgen
Some extra functionality (e.g., generation of POTCARs) do require additional setup (please see the official pymatgen page).
Note: You may need to install numpy before installing pymatgen as numpy’s distutils is needed to compile the spglib and pyhull dependencies.
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 bleeding edge developmental version is at the pymatgen’s Github repo. The developmental version is likely to be more buggy, but may contain new features. The Github version include test files as well for complete unit testing. After cloning the source, you can type:
python setup.py install
or to install the package in developmental mode:
python setup.py develop
To run the very comprehensive suite of unittests, make sure you have nose installed and then just type:
in the pymatgen root directory.
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.
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.
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|pymatgen-2.9.3-py2.7-macosx-10.6-intel.egg (2.0 MB) Copy SHA256 hash SHA256||Egg||2.7||Mar 5, 2014|
|pymatgen-2.9.3.tar.gz (813.7 kB) Copy SHA256 hash SHA256||Source||None||Mar 5, 2014|