scikit-matter

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.

Installation

You can install scikit-matter either via pip using

pip install skmatter

or conda

conda install -c conda-forge skmatter

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

Tests

We are testing our code for Python 3.8 and 3.11 on Windows Server 2019, macOS 11 and Ubuntu LTS 22.04 <https://github.com/actions/runner-images/ blob/main/images/linux/Ubuntu2204-Readme.md>.

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.

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

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

Contributors

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