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

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:

  1. Highly flexible classes for the representation of Element, Site, Molecule, Structure objects.
  2. Extensive input/output support, including support for VASP, ABINIT, 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). 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:

  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 and Appveyor for continuous integration on the Linux and Windows platforms, respectively, 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 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.

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-2022.7.19.tar.gz (2.6 MB view details)

Uploaded Source

Built Distributions

pymatgen-2022.7.19-cp310-cp310-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.10 Windows x86-64

pymatgen-2022.7.19-cp310-cp310-macosx_10_15_universal2.whl (3.3 MB view details)

Uploaded CPython 3.10 macOS 10.15+ universal2 (ARM64, x86-64)

pymatgen-2022.7.19-cp39-cp39-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.9 Windows x86-64

pymatgen-2022.7.19-cp39-cp39-macosx_10_15_x86_64.whl (3.1 MB view details)

Uploaded CPython 3.9 macOS 10.15+ x86-64

pymatgen-2022.7.19-cp38-cp38-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.8 Windows x86-64

pymatgen-2022.7.19-cp38-cp38-macosx_10_15_x86_64.whl (3.1 MB view details)

Uploaded CPython 3.8 macOS 10.15+ x86-64

File details

Details for the file pymatgen-2022.7.19.tar.gz.

File metadata

  • Download URL: pymatgen-2022.7.19.tar.gz
  • Upload date:
  • Size: 2.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for pymatgen-2022.7.19.tar.gz
Algorithm Hash digest
SHA256 4557fb7ee0f25753d97fa0cd8bcfb9d36ea7d422ee12b416cae05c7fb50531d7
MD5 eab330ac87343e3952f9416244d1c5d6
BLAKE2b-256 3874aa837de39307f8452125d82837ecc06b53c617800db3dd101d09dcbd3f0f

See more details on using hashes here.

File details

Details for the file pymatgen-2022.7.19-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pymatgen-2022.7.19-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 3b501f89af0acc6fd92bba5247e96b9b3090c1aeefffe8ea6ed6343c61f09d5b
MD5 4f66bfb05b50bca6e66617371e0fc95c
BLAKE2b-256 f64e6e560433d485dc9731dafaf6c47ec07e97e29da232e44c7a21c24315d404

See more details on using hashes here.

File details

Details for the file pymatgen-2022.7.19-cp310-cp310-macosx_10_15_universal2.whl.

File metadata

File hashes

Hashes for pymatgen-2022.7.19-cp310-cp310-macosx_10_15_universal2.whl
Algorithm Hash digest
SHA256 c245c84295711198f0d956fb831a4b4dbbf636fa5bc2711e0ad0fb62ba1ade61
MD5 461f11c7b8163547bfa45798526d9ef3
BLAKE2b-256 341a758d58df905794cb3c55627e8f59383c746a6c7c8c4634753d9334e99a85

See more details on using hashes here.

File details

Details for the file pymatgen-2022.7.19-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for pymatgen-2022.7.19-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 a67791c4491c5b7cee62acbd5139050adfb522a59c31ccd8bd1235637e191094
MD5 ad9cf49557cb124045575a17b0a96874
BLAKE2b-256 070d986359765a7e4fa73c4cae9b14b190544444bd3987f84f49b43f442a7d36

See more details on using hashes here.

File details

Details for the file pymatgen-2022.7.19-cp39-cp39-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pymatgen-2022.7.19-cp39-cp39-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 1e500bf076fb7277bb3f47a2b511820f42520723f87808860dbfece5b87dcd4c
MD5 3598948bbb775a224667286e43de4296
BLAKE2b-256 4f55e2ad7cf5e840ebc375f2a42a2e795ea3dd5d63594b8ea133ea06bf373990

See more details on using hashes here.

File details

Details for the file pymatgen-2022.7.19-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for pymatgen-2022.7.19-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 ce8b2b55a954777afde88bf2899286bdd79d7400123a7452d3e6dd8ea907f698
MD5 89e28252e1535d327b706b4a7896db13
BLAKE2b-256 4ff1fc5baf55fbb5c69a5506fb30215ef08d71ccb7f53e822ad739cd4e8c7621

See more details on using hashes here.

File details

Details for the file pymatgen-2022.7.19-cp38-cp38-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pymatgen-2022.7.19-cp38-cp38-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 20f07d939de43eb10fa8cb2f6d3b42fcbc38920064dbe5c1550ab001b74d6273
MD5 10a82c73407061157c508af59650e9c1
BLAKE2b-256 5aef9ce06016ad4988bd5ec64f2bd0d0cfc8a832d61f3b60113e9e0070e365f2

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