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arXiv · 1904.08875

DScribe: Library of Descriptors for Machine Learning in Materials Science

Lauri Himanen·Marc O. J. Jäger·Eiaki V. Morooka·Filippo Federici Canova·Yashasvi S. Ranawat·David Z. Gao·Patrick Rinke·Adam S. Foster
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Abstract

DScribe is a software package for machine learning that provides popular feature transformations ("descriptors") for atomistic materials simulations. DScribe accelerates the application of machine learning for atomistic property prediction by providing user-friendly, off-the-shelf descriptor implementations. The package currently contains implementations for Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Function (ACSF) and Smooth Overlap of Atomic Positions (SOAP). Usage of the package is illustrated for two different applications: formation energy prediction for solids and ionic charge prediction for atoms in organic molecules. The package is freely available under the open-source Apache License 2.0.

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Lauri Himanen, Marc O. J. Jäger, Eiaki V. Morooka, Filippo Federici Canova, Yashasvi S. Ranawat, David Z. Gao, Patrick Rinke, Adam S. Foster. 2019-04-18. DScribe: Library of Descriptors for Machine Learning in Materials Science. https://doi.org/10.1016/j.cpc.2019.106949

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