  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.

Materials Science;Machine Learning