scikit-matter

A collection of scikit-learn compatible utilities that implement methods born out of the materials science and chemistry communities

https://github.com/scikit-learn-contrib/scikit-matter

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A collection of scikit-learn compatible utilities that implement methods born out of the materials science and chemistry communities

README.rst

scikit-matter
=============

|tests| |codecov| |pypi| |conda| |docs-stable| |docs-latest| |doi|

A collection of ``scikit-learn`` compatible utilities that implement methods born out of
the materials science and chemistry communities.

For details, tutorials, and examples, please have a look at our documentation_. We also
provide a `latest documentation`_ from the current unreleased development version.

.. _`documentation`: https://scikit-matter.readthedocs.io/en/v0.3.1/
.. _`latest documentation`: https://scikit-matter.readthedocs.io/en/latest

.. marker-installation

Installation
------------
You can install *scikit-matter* either via pip using

.. code-block:: bash

    pip install skmatter

or conda

.. code-block:: bash

    conda install -c conda-forge skmatter

You can then ``import skmatter`` and use scikit-matter in your projects!

.. marker-ci-tests

Tests
-----
We are testing our code for Python 3.10 and 3.13 on the latest versions of Ubuntu,
macOS and Windows.

.. marker-issues

Having problems or ideas?
-------------------------
Having a problem with scikit-matter? Please let us know by `submitting an issue
`_.

Submit new features or bug fixes through a `pull request
`_.

.. marker-contributing

Call for Contributions
----------------------
We always welcome new contributors. If you want to help us take a look at our
`contribution guidelines`_ and afterwards you may start with an open issue marked as
`good first issue`_.

Writing code is not the only way to contribute to the project. You can also:

* review `pull requests`_
* help us stay on top of new and old `issues`_
* develop `examples and tutorials`_
* maintain and `improve our documentation`_
* contribute `new datasets`_

.. _`contribution guidelines`: https://scikit-matter.readthedocs.io/en/latest/contributing.html
.. _`good first issue`: https://github.com/scikit-learn-contrib/scikit-matter/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22
.. _`pull requests`: https://github.com/scikit-learn-contrib/scikit-matter/pulls
.. _`issues`: https://github.com/scikit-learn-contrib/scikit-matter/issues
.. _`improve our documentation`: https://scikit-matter.readthedocs.io/en/latest/contributing.html#contributing-to-the-documentation
.. _`examples and tutorials`: https://scikit-matter.readthedocs.io/en/latest/contributing.html#contributing-new-examples
.. _`new datasets`: https://scikit-matter.readthedocs.io/en/latest/contributing.html#contributing-datasets

.. marker-citing

Citing scikit-matter
--------------------
If you use *scikit-matter* for your work, please cite:

Goscinski A, Principe VP, Fraux G et al. scikit-matter :
A Suite of Generalisable Machine Learning Methods Born out of Chemistry
and Materials Science. Open Res Europe 2023, 3:81.
`10.12688/openreseurope.15789.2`_

.. _`10.12688/openreseurope.15789.2`: https://doi.org/10.12688/openreseurope.15789.2

.. marker-contributors

Contributors
------------
Thanks goes to all people that make scikit-matter possible:

.. image:: https://contrib.rocks/image?repo=scikit-learn-contrib/scikit-matter
   :target: https://github.com/scikit-learn-contrib/scikit-matter/graphs/contributors

.. |tests| image:: https://github.com/scikit-learn-contrib/scikit-matter/workflows/Tests/badge.svg
   :alt: Github Actions Tests Job Status
   :target: action_

.. |codecov| image:: https://codecov.io/gh/scikit-learn-contrib/scikit-matter/branch/main/graph/badge.svg?token=UZJPJG34SM
   :alt: Code coverage
   :target: https://codecov.io/gh/scikit-learn-contrib/scikit-matter/

.. |docs-stable| image:: https://img.shields.io/badge/📚_Documentation-stable-sucess
   :alt: Documentation of stable released version
   :target: `documentation`_

.. |docs-latest| image:: https://img.shields.io/badge/📒_Documentation-latest-yellow.svg
   :alt: Documentation of latest unreleased version
   :target: `latest documentation`_

.. |pypi| image:: https://img.shields.io/pypi/v/skmatter.svg
   :alt: Latest PYPI version
   :target: https://pypi.org/project/skmatter

.. |conda| image:: https://anaconda.org/conda-forge/skmatter/badges/version.svg
   :alt: Latest conda version
   :target: https://anaconda.org/conda-forge/skmatter

.. |doi| image:: https://img.shields.io/badge/DOI-10.12688-blue
   :alt: ORE Paper
   :target: `10.12688/openreseurope.15789.2`_

.. _`action`: https://github.com/scikit-learn-contrib/scikit-matter/actions?query=branch%3Amain

Owner

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use scikit-matter for your work, please read and cite it as below."
title: >-
  scikit-matter : a suite of generalisable machine learning methods born out of chemistry and materials science [version 2; peer review: 3 approved, 1 approved with reservations]
type: journalArticle
issue: 81
volume: 3
authors: 
  - family-names: Goscinski
    given-names: Alexander
  - family-names: Principe
    given-names: Victor P.
  - family-names: Fraux
    given-names: Guillaume
  - family-names: Kliavinek
    given-names: Sergei
  - family-names: Helfrecht
    given-names: Benjamin A.
  - family-names: Loche
    given-names: Philip
  - family-names: Ceriotti
    given-names: Michele
  - family-names: Cersonsky
    given-names: Rose K.
date-published: 2023
identifiers: 
  - type: doi
    value: 10.12688/openreseurope.15789.2

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