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DScribe: Library of descriptors for machine learning in materials science
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en
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12
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Computer Physics Communications, Volume 247
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. Program summary: Program Title: DScribe Program Files doi: http://dx.doi.org/10.17632/vzrs8n8pk6.1 Licensing provisions: Apache-2.0 Programming language: Python/C/C++ Supplementary material: Supplementary Information as PDF Nature of problem: The application of machine learning for materials science is hindered by the lack of consistent software implementations for feature transformations. These feature transformations, also called descriptors, are a key step in building machine learning models for property prediction in materials science. Solution method: We have developed a library for creating common descriptors used in machine learning applied to materials science. We provide an implementation the following descriptors: Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Functions (ACSF) and Smooth Overlap of Atomic Positions (SOAP). The library has a python interface with computationally intensive routines written in C or C++. The source code, tutorials and documentation are provided online. A continuous integration mechanism is set up to automatically run a series of regression tests and check code coverage when the codebase is updated.
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| openaire: EC/H2020/676580/EU//NoMaD | openaire: EC/H2020/686053/EU//CritCat
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Himanen, L, Jäger, M O J, Morooka, E V, Federici Canova, F, Ranawat, Y S, Gao, D Z, Rinke, P & Foster, A S 2020, 'DScribe : Library of descriptors for machine learning in materials science', Computer Physics Communications, vol. 247, 106949. https://doi.org/10.1016/j.cpc.2019.106949