scikit-matter documentation¶
scikit-matter is a collection of scikit-learn
compatible utilities that implement methods born out of the materials science
and chemistry communities.
Convenient-to-use libraries such as scikit-learn have accelerated the adoption and application of machine learning (ML) workflows and data-driven methods. Such libraries have gained great popularity partly because the implemented methods are generally applicable in multiple domains. While developments in the atomistic learning community have put forward general-use machine learning methods, their deployment is commonly entangled with domain-specific functionalities, preventing access to a wider audience.
scikit-matter targets domain-agnostic implementations of methods developed in the computational chemical and materials science community, following the scikit-learn API and coding guidelines to promote usability and interoperability with existing workflows. scikit-matter contains a toolbox of methods for unsupervised and supervised analysis of ML datasets, including the comparison, decomposition, and selection of features and samples.