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 impleme...
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| Veröffentlicht in: | Computer physics communications Jg. 247; S. 106949 |
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| Hauptverfasser: | , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.02.2020
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| Schlagworte: | |
| ISSN: | 0010-4655, 1879-2944 |
| Online-Zugang: | Volltext |
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| 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 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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| AbstractList | 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 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. |
| ArticleNumber | 106949 |
| Author | Jäger, Marc O.J. Federici Canova, Filippo Himanen, Lauri Morooka, Eiaki V. Gao, David Z. Rinke, Patrick Ranawat, Yashasvi S. Foster, Adam S. |
| Author_xml | – sequence: 1 givenname: Lauri surname: Himanen fullname: Himanen, Lauri email: lauri.himanen@aalto.fi organization: Department of Applied Physics, Aalto University, P.O. Box 11100, 00076 Aalto, Espoo, Finland – sequence: 2 givenname: Marc O.J. surname: Jäger fullname: Jäger, Marc O.J. organization: Department of Applied Physics, Aalto University, P.O. Box 11100, 00076 Aalto, Espoo, Finland – sequence: 3 givenname: Eiaki V. surname: Morooka fullname: Morooka, Eiaki V. organization: Department of Applied Physics, Aalto University, P.O. Box 11100, 00076 Aalto, Espoo, Finland – sequence: 4 givenname: Filippo surname: Federici Canova fullname: Federici Canova, Filippo organization: Department of Applied Physics, Aalto University, P.O. Box 11100, 00076 Aalto, Espoo, Finland – sequence: 5 givenname: Yashasvi S. orcidid: 0000-0001-7799-4267 surname: Ranawat fullname: Ranawat, Yashasvi S. organization: Department of Applied Physics, Aalto University, P.O. Box 11100, 00076 Aalto, Espoo, Finland – sequence: 6 givenname: David Z. surname: Gao fullname: Gao, David Z. organization: Nanolayers Research Computing Ltd., 1 Granville Court, Granville Road, London, N12 0HL, United Kingdom – sequence: 7 givenname: Patrick surname: Rinke fullname: Rinke, Patrick organization: Department of Applied Physics, Aalto University, P.O. Box 11100, 00076 Aalto, Espoo, Finland – sequence: 8 givenname: Adam S. orcidid: 0000-0001-5371-5905 surname: Foster fullname: Foster, Adam S. organization: Department of Applied Physics, Aalto University, P.O. Box 11100, 00076 Aalto, Espoo, Finland |
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