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://materialsproject.org).
Project description
Official docs: https://pymatgen.org
Pymatgen (Python Materials Genomics) is a robust, open-source Python library for materials analysis. These are some of the main features:
- Highly flexible classes for the representation of Element, Site, Molecule, Structure objects.
- Extensive input/output support, including support for VASP, ABINIT, CIF, Gaussian, XYZ, and many other file formats.
- Powerful analysis tools, including generation of phase diagrams, Pourbaix diagrams, diffusion analyses, reactions, etc.
- Electronic structure analyses, such as density of states and band structure.
- 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 to the pymatgen repo. For help with any pymatgen issues, please use the Discourse page.
Why use pymatgen?
There are many materials analysis codes out there, both commercial and free, but pymatgen offer several advantages:
- 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 and Appveyor for continuous integration on the Linux and Windows platforms, respectively, which ensures that every commit passes a comprehensive suite of unittests.
- It is well documented. A fairly comprehensive documentation has been written to help you get to grips with it quickly.
- 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.
- 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.
- 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.
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