Matrix multiplication

Quantum mechanics

Computational science

Algorithm

Linguistics

Philosophy

Materials science

Chromatography

Implementation

Computer science

Matrix (chemical analysis)

Software

Tensor (intrinsic definition)

Physics

Mathematics

Feature (linguistics)

Artificial intelligence

Geometry

Quantum

Composite material

Chemistry

Programming language

Computer Physics Communications

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.

DScribe: Library of descriptors for machine learning in materials science