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: 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, 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 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 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-2022.1.20.tar.gz (2.6 MB view details)

Uploaded Source

Built Distributions

pymatgen-2022.1.20-cp310-cp310-win_amd64.whl (3.0 MB view details)

Uploaded CPython 3.10 Windows x86-64

pymatgen-2022.1.20-cp310-cp310-macosx_10_15_x86_64.whl (3.1 MB view details)

Uploaded CPython 3.10 macOS 10.15+ x86-64

pymatgen-2022.1.20-cp39-cp39-win_amd64.whl (3.0 MB view details)

Uploaded CPython 3.9 Windows x86-64

pymatgen-2022.1.20-cp39-cp39-macosx_11_0_arm64.whl (3.0 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

pymatgen-2022.1.20-cp39-cp39-macosx_10_15_x86_64.whl (3.1 MB view details)

Uploaded CPython 3.9 macOS 10.15+ x86-64

pymatgen-2022.1.20-cp38-cp38-win_amd64.whl (3.0 MB view details)

Uploaded CPython 3.8 Windows x86-64

pymatgen-2022.1.20-cp38-cp38-macosx_10_14_x86_64.whl (3.1 MB view details)

Uploaded CPython 3.8 macOS 10.14+ x86-64

File details

Details for the file pymatgen-2022.1.20.tar.gz.

File metadata

  • Download URL: pymatgen-2022.1.20.tar.gz
  • Upload date:
  • Size: 2.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.9.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pymatgen-2022.1.20.tar.gz
Algorithm Hash digest
SHA256 faef8465adfce3fd15a2af9bf85a352868ceeeee1babec5ad0d831464973b851
MD5 8e66bac1a37dffbe49be03299472cf3d
BLAKE2b-256 461a0a6315bb801c9d7ae949d13d7326ebb52e1c01ac6869a55437cacacb4a5c

See more details on using hashes here.

File details

Details for the file pymatgen-2022.1.20-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: pymatgen-2022.1.20-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 3.0 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.1

File hashes

Hashes for pymatgen-2022.1.20-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 93eb14e1595ff30ef031b0c265676a82bbb7a8cff27ca98fcd3d4ac5cf473b29
MD5 02968ffd2120437ef6ac2da3edf4a289
BLAKE2b-256 0a0c01ee5d6607b055164f921477f2c9726b245257b0cc16211bebef924b5ef8

See more details on using hashes here.

File details

Details for the file pymatgen-2022.1.20-cp310-cp310-macosx_10_15_x86_64.whl.

File metadata

  • Download URL: pymatgen-2022.1.20-cp310-cp310-macosx_10_15_x86_64.whl
  • Upload date:
  • Size: 3.1 MB
  • Tags: CPython 3.10, macOS 10.15+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.2

File hashes

Hashes for pymatgen-2022.1.20-cp310-cp310-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 efdcf71e78363ff7261dc85d321d65b294b0b8dfa0af75d3a575a2854de8320a
MD5 5e53d93576c6f9c385a352c4f8ab0900
BLAKE2b-256 6e3c417193fb36bede99194f153a16993db484d98679971e3837d40ae78e42f9

See more details on using hashes here.

File details

Details for the file pymatgen-2022.1.20-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: pymatgen-2022.1.20-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 3.0 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9

File hashes

Hashes for pymatgen-2022.1.20-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 8b82c271a38d289ce36d94b0942957d887331cd82070939c60c370a5e0c1ecfd
MD5 2750d2ea05ca8cd8c29a0c13962ba047
BLAKE2b-256 90464717a5df1df9e7abcc191cbac5566b71910eef8723d796ae51216e5beea2

See more details on using hashes here.

File details

Details for the file pymatgen-2022.1.20-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

  • Download URL: pymatgen-2022.1.20-cp39-cp39-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 3.0 MB
  • Tags: CPython 3.9, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.9.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pymatgen-2022.1.20-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 9f3f171f22eb4abb1f66d16a7c19762f5fa08f811d32df7ea210718516d8b824
MD5 1b5a6a5b5629e02342838e0976388fc9
BLAKE2b-256 c04c26a2135422d84889938943cd28f286779aee4e555d67edb5b6b5ae6814da

See more details on using hashes here.

File details

Details for the file pymatgen-2022.1.20-cp39-cp39-macosx_10_15_x86_64.whl.

File metadata

  • Download URL: pymatgen-2022.1.20-cp39-cp39-macosx_10_15_x86_64.whl
  • Upload date:
  • Size: 3.1 MB
  • Tags: CPython 3.9, macOS 10.15+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.10

File hashes

Hashes for pymatgen-2022.1.20-cp39-cp39-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 ae8d1d701e7d74feeb80c515f672a380ad589c50d85786fcbb8019cc07807715
MD5 92830e38627bf4c08d8f70b631dd42f4
BLAKE2b-256 2ffa8ca7e8ee57e2311508a26ab1917f3453c2a7ce75d9c43708970fb46de733

See more details on using hashes here.

File details

Details for the file pymatgen-2022.1.20-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: pymatgen-2022.1.20-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 3.0 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.10

File hashes

Hashes for pymatgen-2022.1.20-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 222df15a3618c694d05c43c1bb2a3bd1a2c8f2cdd3d8e0aaa664937765bcd593
MD5 4bc1d7640389c7d2e5be2e3a2e9c72ea
BLAKE2b-256 2cf6334c5e1829e40e07bd2d7298a7501b9a6f14d8b7374976565821e993da75

See more details on using hashes here.

File details

Details for the file pymatgen-2022.1.20-cp38-cp38-macosx_10_14_x86_64.whl.

File metadata

  • Download URL: pymatgen-2022.1.20-cp38-cp38-macosx_10_14_x86_64.whl
  • Upload date:
  • Size: 3.1 MB
  • Tags: CPython 3.8, macOS 10.14+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.1 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12

File hashes

Hashes for pymatgen-2022.1.20-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 a9186470167c9a14359fa05db5e3bea0793e89a8edca698037ba11539b8b111d
MD5 a3858284e50ee605d1621d1b364e7ae9
BLAKE2b-256 6e3d9d1a2e680096506bb7e30225402c9cfcfcd6c37f20ff5c683e014a19633c

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