DScribe: Library of descriptors for machine learning in materials science

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…

• EC
  European Commission

  Awards: 686053, 676580
• H2
  Horizon 2020 Framework Programme
• JJ
  Jenny ja Antti Wihurin Rahasto
• H2
  Horizon 2020

  Awards: 676580 NOMAD, 686053 CRITCAT