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Rascaline is a library for the efficient computing of representations for atomistic machine learning also called "descriptors" or "fingerprints". These representations can be used for atomistic machine learning (ml) models including ml potentials, visualization or similarity analysis.
The core of the library is written in Rust and we provide APIs for C/C++ and Python as well.
.. warning::
**Rascaline is still as the proof of concept stage. You should not use it for
anything important.**
List of implemented representations ###################################
.. inclusion-marker-representations-start
.. list-table:: :widths: 25 50 20 :header-rows: 1
.. inclusion-marker-representations-end
For details, tutorials, and examples, please have a look at our documentation
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.. _documentation
: https://luthaf.fr/rascaline/index.html
.. |test| image:: https://img.shields.io/github/check-runs/Luthaf/rascaline/master?logo=github&label=tests :alt: Tests status :target: https://github.com/Luthaf/rascaline/actions?query=branch%3Amaster
.. |docs| image:: https://img.shields.io/badge/documentation-latest-sucess
:alt: Documentation
:target: documentation
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.. |cov| image:: https://codecov.io/gh/Luthaf/rascaline/branch/master/graph/badge.svg :alt: Coverage Status :target: https://codecov.io/gh/Luthaf/rascaline