A collection of scikit-learn compatible utilities that implement methods born out of the materials science and chemistry communities.
Project description
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.
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
Contributors
Thanks goes to all people that make scikit-matter possible:
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