swMATH ID: 32471
Software Authors: Lauri Himanen, Marc O. J. Jäger, Eiaki V. Morooka, Filippo Federici Canova, Yashasvi S. Ranawat, David Z. Gao, Patrick Rinke, Adam S. Foster
Description: 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 freely available under the open-source Apache License 2.0.
Homepage: https://arxiv.org/abs/1904.08875
Source Code:  https://github.com/SINGROUP/dscribe
Dependencies: Python
Related Software: Python; Scikit; SciPy; ESPResSo++; LAMMPS; OVITO; Mdtraj; ESPResSo; atooms; HOOMD-blue; partycls; Amp; Matplotlib; Adam; pymatgen; Dask; NumPy; ChemML; DeepChem; TensorFlow
Cited in: 1 Publication

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