Python Materials Genomics is a robust materials analysis code that defines core object representations for structures

Navigation

Verified details

These details have been verified by PyPI
Project links
GitHub Statistics
Maintainers
Avatar for janosh from gravatar.com janosh Avatar for mkhorton from gravatar.com mkhorton Avatar for Shyue.Ping.Ong from gravatar.com Shyue.Ping.Ong

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (MIT)
  • Author: Pymatgen Development Team
  • Maintainer: Janosh Riebesell
  • Tags ABINIT, VASP, analysis, crystal, diagrams, electronic, gaussian, materials, nwchem, phase, project, qchem, science, structure
  • Requires: Python >=3.10
  • Provides-Extra: abinit, ase, ci, docs, electronic-structure, mlp, numba, numpy-v1, optional, tblite, vis
Classifiers

Project description

Logo

CI Status codecov PyPI Downloads Conda Downloads Requires Python 3.10+ Paper

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 and 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 contributions. These contributions can be in the form of additional tools or modules you develop, or feature requests and bug reports. The following are resources for pymatgen:

Why use pymatgen?

  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 Github Actions for continuous integration, which ensures that every new code passes a comprehensive suite of unit tests.
  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 fast. 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.
  6. A growing ecosystem of developers and add-ons. Pymatgen has contributions from materials scientists all over the world. We also now have an architecture to support add-ons that expand pymatgen's functionality even further. Check out the contributing page and add-ons page for details and examples.

Installation

The version at the Python Package Index PyPI is always the latest stable release that is relatively bug-free and can be installed via pip:

pip install pymatgen

If you'd like to use the latest unreleased changes on the main branch, you can install directly from GitHub:

pip install -U git+https://github.com/materialsproject/pymatgen

The minimum Python version is 3.10. Some extra functionality (e.g., generation of POTCARs) does require additional setup (see the pymatgen docs).

Change Log

See GitHub releases, docs/CHANGES.md or commit history in increasing order of details.

Using pymatgen

Please refer to the official pymatgen docs 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 the pymatgen docs on how to cite them.

Soliciting contributions to 2nd pymatgen paper

If you are a long-standing pymatgen contributor and would like to be involved in working on an updated pymatgen publication, please fill out this co-author registration form or contact @shyuep, @mkhorton and @janosh with questions.

License

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.

About the Pymatgen Development Team

Shyue Ping Ong (@shyuep) of the Materials Virtual Lab started Pymatgen in 2011 and is still the project lead. Janosh Riebesell (@janosh) and Matthew Horton (@mkhorton) are co-maintainers.

The pymatgen development team is the set of all contributors to the pymatgen project, including all subprojects.

Our Copyright Policy

Pymatgen uses a shared copyright model. Each contributor maintains copyright over their contributions to pymatgen. But, it is important to note that these contributions are typically only changes to the repositories. Thus, the pymatgen source code, in its entirety is not the copyright of any single person or institution. Instead, it is the collective copyright of the entire pymatgen Development Team. If individual contributors want to maintain a record of what changes/contributions they have specific copyright on, they should indicate their copyright in the commit message of the change, when they commit the change to one of the pymatgen repositories.

Project details

Verified details

These details have been verified by PyPI
Project links
GitHub Statistics
Maintainers
Avatar for janosh from gravatar.com janosh Avatar for mkhorton from gravatar.com mkhorton Avatar for Shyue.Ping.Ong from gravatar.com Shyue.Ping.Ong

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (MIT)
  • Author: Pymatgen Development Team
  • Maintainer: Janosh Riebesell
  • Tags ABINIT, VASP, analysis, crystal, diagrams, electronic, gaussian, materials, nwchem, phase, project, qchem, science, structure
  • Requires: Python >=3.10
  • Provides-Extra: abinit, ase, ci, docs, electronic-structure, mlp, numba, numpy-v1, optional, tblite, vis
Classifiers

Release history Release notifications | RSS feed

2024.11.13

2024.10.29

2024.10.27

2024.10.25

2024.10.22

2024.10.3

This version

2024.9.17.1

2024.9.17

2024.9.10

2024.8.9

2024.8.8 yanked

Reason this release was yanked:

Backward incompatible change introduced.

2024.7.18

2024.6.10

2024.6.4

2024.5.31 yanked

2024.5.1

2024.4.13

2024.4.12

2024.3.1

2024.2.23

2024.2.20

2024.2.8

2024.1.27

2024.1.26

2023.12.18

2023.11.12

2023.11.10

2023.10.11

2023.10.4

2023.10.3

2023.9.25

2023.9.10

2023.9.2

2023.8.10

2023.7.20

2023.7.17

2023.7.14

2023.7.11

2023.6.28 yanked

Reason this release was yanked:

Bad release without wheels for some systems.

2023.6.23

2023.5.31

2023.5.10

2023.5.8

2023.3.23

2023.3.10

2023.2.28

2023.2.22

2023.1.30

2023.1.20

2023.1.9

2022.11.7

2022.11.1

2022.10.22

2022.9.21

2022.9.8

2022.8.23

2022.7.25

2022.7.24.1

2022.7.24 yanked

Reason this release was yanked:

remove defunct MPRester2 in favor of more streamlined handling of new and legacy API

2022.7.19

2022.7.8

2022.5.26

2022.5.19

2022.5.18.1

2022.5.18

2022.5.17

2022.4.26

2022.4.19

2022.3.29

2022.3.24

2022.3.22

2022.3.7

2022.2.10

2022.2.7

2022.2.1

2022.1.24

2022.1.20

2022.1.9

2022.1.8

2022.0.17

2022.0.16

2022.0.15

2022.0.14

2022.0.13

2022.0.12

2022.0.11

2022.0.10

2022.0.9

2022.0.8

2022.0.7

2022.0.6

2022.0.5

2022.0.4

2022.0.3

2022.0.2 yanked

Reason this release was yanked:

Buggy version

2022.0.1 yanked

Reason this release was yanked:

Buggy version

2022.0.0 yanked

Reason this release was yanked:

Buggy version

2021.3.9

2021.3.5

2021.3.4 yanked

Reason this release was yanked:

Breaking change in this version. Reverted in 2021.3.5.

2021.3.3

2021.2.16

2021.2.14

2021.2.13

2021.2.8.1

2021.2.8

2020.12.31

2020.12.18

2020.12.3

2020.11.11

2020.10.20

2020.10.9.1

2020.10.9

2020.9.14

2020.8.13

2020.8.3

2020.7.18

2020.7.16

2020.7.14

2020.7.10

2020.7.3

2020.6.8

2020.4.29

2020.4.2

2020.3.13

2020.3.2

2020.1.28

2020.1.10

2019.12.22

2019.12.3

2019.11.11

2019.10.16

2019.10.4

2019.10.3

2019.10.2

2019.9.16

2019.9.12

2019.9.8

2019.9.7

2019.8.23

2019.8.14

2019.7.30

2019.7.21

2019.7.2

2019.6.20

2019.6.5

2019.5.28

2019.5.8

2019.5.1

2019.4.11

2019.3.27

2019.3.13

2019.2.28

2019.2.24

2019.2.4

2019.1.24

2019.1.13

2018.12.12

2018.11.30

2018.11.6

2018.10.18

2018.9.30

2018.9.19

2018.9.12

2018.9.1

2018.8.10

2018.8.7

2018.7.23

2018.7.15

2018.6.27

2018.6.11

2018.5.22

2018.5.21

2018.5.14

2018.5.3

2018.4.20

2018.4.6

2018.3.23

2018.3.14

2018.3.13

2018.3.2

2018.2.13

2018.1.29

2018.1.19

2017.12.30

2017.12.16

2017.12.15

2017.12.8

2017.12.6

2017.11.30

2017.11.27

2017.11.9

2017.11.6

2017.10.16

2017.9.23

2017.9.3

2017.9.1

2017.8.21

2017.8.20

2017.8.16

2017.8.14

2017.8.4

2017.7.21

2017.7.4

2017.6.24

2017.6.22

2017.6.8

4.7.7

4.7.6

4.7.5

4.7.4

4.7.3

4.7.2

4.7.1

4.7.0

4.6.2

4.6.1

4.6.0

4.5.7

4.5.6

4.5.5

4.5.4

4.5.3

4.5.2

4.5.1

4.5.0

4.4.12

4.4.11

4.4.10

4.4.9

4.4.8

4.4.7

4.4.6

4.4.5

4.4.4

4.4.3

4.4.2

4.4.1

4.4.0

4.3.2

4.3.1

4.3.0

4.2.5

4.2.4

4.2.3

4.2.2

4.2.1

4.2.0

4.1.1

4.1.0

4.0.2

4.0.1

4.0.0

3.7.1

3.7.0

3.6.1

3.6.0

3.5.3

3.5.2

3.5.1

3.5.0

3.4.0

3.3.6

3.3.5

3.3.4

3.3.3

3.3.2

3.3.1

3.3.0

3.2.10

3.2.9

3.2.8

3.2.7

3.2.6

3.2.5

3.2.4

3.2.3

3.2.2

3.2.1

3.2.0

3.1.9

3.1.8

3.1.7

3.1.6

3.1.5

3.1.4

3.1.3

3.1.2

3.1.1

3.1.0

3.0.13

3.0.12

3.0.11

3.0.10

3.0.9

3.0.8

3.0.7

3.0.6

3.0.5

3.0.4

3.0.3

3.0.2

3.0.1

3.0.0

2.10.6

2.10.5

2.10.4

2.10.3

2.10.2

2.10.1

2.10.0

2.9.14

2.9.13

2.9.12

2.9.11

2.9.10

2.9.9

2.9.8

2.9.7

2.9.6

2.9.5

2.9.4

2.9.3

2.9.2

2.9.1

2.9.0

2.8.10

2.8.9

2.8.8

2.8.7

2.8.6

2.8.5

2.8.3

2.8.2

2.8.1

2.8.0

2.7.9

2.7.8

2.7.7

2.7.6

2.7.5

2.7.4

2.7.3

2.7.2b pre-release

2.7.1

2.7.0

2.6.6

2.6.5

2.6.4

2.6.3

2.6.2

2.6.1

2.5.5

2.5.4

2.5.3

2.5.2

2.5.1

2.5.0

2.4.3

2.4.2

2.4.1

2.4.0

2.3.2

2.3.1

2.3.0

2.2.6

2.2.4

2.2.3

2.2.2

2.2.1

2.2.0

2.1.2

2.1.0

2.0.0

1.9.0

1.8.3

1.8.2

1.8.0

1.7.2

1.7.0

1.6.0

1.5.0

1.2.9

1.2.8

1.2.4

1.2.3

1.2.2

1.2.1

1.2.0

1.1.2

1.1.1

1.1.0

1.0.5

1.0.4

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pymatgen-2024.9.17.1.tar.gz (3.1 MB view details)

Uploaded Source

Built Distributions

pymatgen-2024.9.17.1-cp312-cp312-win_amd64.whl (3.6 MB view details)

Uploaded CPython 3.12 Windows x86-64

pymatgen-2024.9.17.1-cp312-cp312-win32.whl (3.6 MB view details)

Uploaded CPython 3.12 Windows x86

pymatgen-2024.9.17.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.0 MB view details)

Uploaded CPython 3.12 manylinux: glibc 2.17+ x86-64

pymatgen-2024.9.17.1-cp312-cp312-macosx_11_0_arm64.whl (3.6 MB view details)

Uploaded CPython 3.12 macOS 11.0+ ARM64

pymatgen-2024.9.17.1-cp311-cp311-win_amd64.whl (3.6 MB view details)

Uploaded CPython 3.11 Windows x86-64

pymatgen-2024.9.17.1-cp311-cp311-win32.whl (3.6 MB view details)

Uploaded CPython 3.11 Windows x86

pymatgen-2024.9.17.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.0 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

pymatgen-2024.9.17.1-cp311-cp311-macosx_11_0_arm64.whl (3.6 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

pymatgen-2024.9.17.1-cp310-cp310-win_amd64.whl (3.6 MB view details)

Uploaded CPython 3.10 Windows x86-64

pymatgen-2024.9.17.1-cp310-cp310-win32.whl (3.6 MB view details)

Uploaded CPython 3.10 Windows x86

pymatgen-2024.9.17.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

pymatgen-2024.9.17.1-cp310-cp310-macosx_11_0_arm64.whl (3.6 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

File details

Details for the file pymatgen-2024.9.17.1.tar.gz.

File metadata

  • Download URL: pymatgen-2024.9.17.1.tar.gz
  • Upload date:
  • Size: 3.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.5

File hashes

Hashes for pymatgen-2024.9.17.1.tar.gz
Algorithm Hash digest
SHA256 87966bd3839afb32ec30a7548f630452fe98420b97edc9df8d12602b1472975d
MD5 50eb8867d99ffc3707af0128fb219fff
BLAKE2b-256 02efeb4cd347a5faa06b21e88f5036ce8fe18df380b59908700a737508fe93b1

See more details on using hashes here.

File details

Details for the file pymatgen-2024.9.17.1-cp312-cp312-win_amd64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.9.17.1-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 57101b1b93d893da8d9b9de17e74f6fd96c05a06fe236e9716539af1ead4f86d
MD5 29a2429c1b566fe818c4debea2755523
BLAKE2b-256 43cea9294d4792d6bced6e22f7bacb5ea417c43c5d1eb081138b60f798fabb36

See more details on using hashes here.

File details

Details for the file pymatgen-2024.9.17.1-cp312-cp312-win32.whl.

File metadata

File hashes

Hashes for pymatgen-2024.9.17.1-cp312-cp312-win32.whl
Algorithm Hash digest
SHA256 37249e40a224495e2b2971030aaaaca381dabf28bff2f73a71621f5306849b05
MD5 b5e78448536a6634da5b3a6fabc57864
BLAKE2b-256 bcf45ed69fa97169d604328842d9391d542f12dd9633dcc9e9b15c4638fa0faf

See more details on using hashes here.

File details

Details for the file pymatgen-2024.9.17.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.9.17.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 31125995250eb2a5680a5457a1cf113a7adffd14acc42966dc38d781b1a6461c
MD5 6f6c3f7cb62264f3fb8509dfce8ae283
BLAKE2b-256 faaaf981b154dfde99a1c54638319c81785585e3b5b968da43e1b335f294740d

See more details on using hashes here.

File details

Details for the file pymatgen-2024.9.17.1-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.9.17.1-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 96743b65e12f39babfb6e7830831f7a126c7f97a66f183214f3272f30dd10f4e
MD5 8e10cec93b8998169fdae0565caba52a
BLAKE2b-256 6ecffb6e7d518215016d476121a89370a310480ed0b1003e76a8b38519483ab5

See more details on using hashes here.

File details

Details for the file pymatgen-2024.9.17.1-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.9.17.1-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 9f1d0af75eda65ffccc85e31869ae196b26f072c2d4ce0286a48bf892f0a7282
MD5 8782a10e5c4c3a121a6b5838106a85cc
BLAKE2b-256 4aa865b32375dcc9dcde26a411408eec54266820ab519022c89be649b6e58f50

See more details on using hashes here.

File details

Details for the file pymatgen-2024.9.17.1-cp311-cp311-win32.whl.

File metadata

File hashes

Hashes for pymatgen-2024.9.17.1-cp311-cp311-win32.whl
Algorithm Hash digest
SHA256 b79bd616d25ec1ee0bf1cc341ef006a1a2df96cd698d71824eebd2e5747cdd3b
MD5 d595ce6943a5c29c2fec3022da1301bf
BLAKE2b-256 2c5e2fd9b91703c4236a8f6b177dba052adab254e459b8dc02fd32a23038b57a

See more details on using hashes here.

File details

Details for the file pymatgen-2024.9.17.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.9.17.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 81a41317c5acedd3dfa5c4ef978561ee15e68c749d42e6eab7661eacb98bb4aa
MD5 89ac8123e0f42edeeeaa477e36c0ae1f
BLAKE2b-256 02c86dd268a5f71d0b8bbb370a9dcbcfd7501270049a53d1399ed6cf46fff0b1

See more details on using hashes here.

File details

Details for the file pymatgen-2024.9.17.1-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.9.17.1-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 b64df7f9030bd3f109f02d88bbe1d9bbb10adfdd8156ff74d276e350112e3829
MD5 91b54f266ba548f9ed54f067db0cb40d
BLAKE2b-256 7defbb7fa8a1f36bdf1556511aebf5307711f8d88d5e078e3a58abbf5dbe5607

See more details on using hashes here.

File details

Details for the file pymatgen-2024.9.17.1-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.9.17.1-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 228d5757151107dac12a052bc5b0ea7fc35ca2f208da7464b48391ce8414367c
MD5 cf82b3806d41137b8809598ddbc2cf21
BLAKE2b-256 4cf76a3a0213c155ba488bce845115a8e33473f76576d2aa49a48c9428e32e4a

See more details on using hashes here.

File details

Details for the file pymatgen-2024.9.17.1-cp310-cp310-win32.whl.

File metadata

File hashes

Hashes for pymatgen-2024.9.17.1-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 304c07e784c9ab8342086f291bb9ed20257f5bebd2e536b06eb1008f808b503e
MD5 8a52e380edc5d962c4f7fd754ec1c7e2
BLAKE2b-256 1614772aa38f98f64a9cabcfdc55f4f931103b4713105e2e0f8a08f34d42dcc4

See more details on using hashes here.

File details

Details for the file pymatgen-2024.9.17.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.9.17.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b14544c959700e0b8557451ac8403e423f53df0cc2ee13e68dd7f246400a1d0b
MD5 fe36220b1dc742c2164c2d1c13f63a86
BLAKE2b-256 30e971d6f449a0c268503d6b313f89196f3141a9dcb087b98cb540c2282b8e2b

See more details on using hashes here.

File details

Details for the file pymatgen-2024.9.17.1-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pymatgen-2024.9.17.1-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 97497f018472aeed97f866a8b8cb43759d6e70939ab2b6239647bfed01c988a4
MD5 73e9f2f208c102a8e4562709711ba616
BLAKE2b-256 ceda147abd178f7b35e900b385eee60460052debdb31d1d9bd5f168435f6bd0d

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