scikit-matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science

Alexander Goscinski; Victor Paul Principe; Guillaume Fraux; Sergei Kliavinek; Benjamin Aaron Helfrecht; Philip Loche; Michele Ceriotti; Rose Kathleen Cersonsky

doi:10.12688/openreseurope.15789.2

scikit-matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science

Open Res Eur. 2023 Sep 18:3:81. doi: 10.12688/openreseurope.15789.2. eCollection 2023.

Authors

Alexander Goscinski¹, Victor Paul Principe¹, Guillaume Fraux¹, Sergei Kliavinek¹, Benjamin Aaron Helfrecht^{1

2}, Philip Loche¹, Michele Ceriotti¹, Rose Kathleen Cersonsky^{1

3}

Affiliations

¹ Laboratory of Computational Science and Modeling (COSMO), Institute of Materials, Ecole Polytechnique Federale de Lausanne, Lausanne, Vaud, 1015, Switzerland.
² Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
³ Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA.

Abstract

Easy-to-use libraries such as scikit-learn have accelerated the adoption and application of machine learning (ML) workflows and data-driven methods. While many of the algorithms implemented in these libraries originated in specific scientific fields, they have gained in popularity in part because of their generalisability across multiple domains. Over the past two decades, researchers in the chemical and materials science community have put forward general-purpose machine learning methods. The deployment of these methods into workflows of other domains, however, is often burdensome due to the entanglement with domainspecific functionalities. We present the python library scikit-matter that 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.

Keywords: KPCovR; PCovR; Python; directional convex hull; feature reconstruction; feature selection; sample selection.

Grants and funding

V.P.P. and M.C. received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme Grant No. 101001890-FIAMMA. M.C., R.K.C., B.A.H. received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme grant No Grant No. 677013-HBMAP. A.G. and M.C. acknowledge support from the Swiss National Science Foundation (Project No. 200021-182057). G.F. acknowledges support from the Swiss Platform for Advanced Scientific Computing (PASC). R.K.C. acknowledges support from the University of Wisconsin - Madison and the Wisconsin Alumni Research Foundation (WARF)