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.
Installation
You can install scikit-matter either via pip using
pip install skmatteror conda
conda install -c conda-forge skmatterYou can then import skmatter and use scikit-matter in your projects!
Tests
We are testing our code for Python 3.11 and 3.14 on the latest versions of Ubuntu, macOS and Windows.
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:
3 maintainers
Release files for skmatter 0.4.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| skmatter-0.4.1.tar.gz | 1.9 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| skmatter-0.4.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 3.8 MB
Release files / skmatter-0.4.1.tar.gz
| Download URL | skmatter-0.4.1.tar.gz |
|---|---|
| Size | 1.9 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
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BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 27, 2026.
Transparency logRelease files / skmatter-0.4.1-py3-none-any.whl
| Download URL | skmatter-0.4.1-py3-none-any.whl |
|---|---|
| Size | 1.9 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
b222443b1cef1f1eaf732071c80a71fbd8d942352da7cf2a0a1ad4d8b335b8bf
|
|
BLAKE2b-256 checksum How to use checksums |
c9232dae4aaa9a4748e458f6097a8c8c40fbe7dbb329b9f5ae481df9b318fa9a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 27, 2026.
Transparency log
