Automated discovery of a robust interatomic potential for aluminum

The accuracy of a machine-learned potential is limited by the quality and diversity of the training dataset. Here the authors propose an active learning approach to automatically construct general purpose machine-learning potentials here demonstrated for the aluminum case.

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Autores principales:	Justin S. Smith, Benjamin Nebgen, Nithin Mathew, Jie Chen, Nicholas Lubbers, Leonid Burakovsky, Sergei Tretiak, Hai Ah Nam, Timothy Germann, Saryu Fensin, Kipton Barros
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/f563b56affa04410a543a78699a94661
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spelling	oai:doaj.org-article:f563b56affa04410a543a78699a946612021-12-02T16:23:13ZAutomated discovery of a robust interatomic potential for aluminum10.1038/s41467-021-21376-02041-1723https://doaj.org/article/f563b56affa04410a543a78699a946612021-02-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-21376-0https://doaj.org/toc/2041-1723The accuracy of a machine-learned potential is limited by the quality and diversity of the training dataset. Here the authors propose an active learning approach to automatically construct general purpose machine-learning potentials here demonstrated for the aluminum case.Justin S. SmithBenjamin NebgenNithin MathewJie ChenNicholas LubbersLeonid BurakovskySergei TretiakHai Ah NamTimothy GermannSaryu FensinKipton BarrosNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Justin S. Smith Benjamin Nebgen Nithin Mathew Jie Chen Nicholas Lubbers Leonid Burakovsky Sergei Tretiak Hai Ah Nam Timothy Germann Saryu Fensin Kipton Barros Automated discovery of a robust interatomic potential for aluminum
description	The accuracy of a machine-learned potential is limited by the quality and diversity of the training dataset. Here the authors propose an active learning approach to automatically construct general purpose machine-learning potentials here demonstrated for the aluminum case.
format	article
author	Justin S. Smith Benjamin Nebgen Nithin Mathew Jie Chen Nicholas Lubbers Leonid Burakovsky Sergei Tretiak Hai Ah Nam Timothy Germann Saryu Fensin Kipton Barros
author_facet	Justin S. Smith Benjamin Nebgen Nithin Mathew Jie Chen Nicholas Lubbers Leonid Burakovsky Sergei Tretiak Hai Ah Nam Timothy Germann Saryu Fensin Kipton Barros
author_sort	Justin S. Smith
title	Automated discovery of a robust interatomic potential for aluminum
title_short	Automated discovery of a robust interatomic potential for aluminum
title_full	Automated discovery of a robust interatomic potential for aluminum
title_fullStr	Automated discovery of a robust interatomic potential for aluminum
title_full_unstemmed	Automated discovery of a robust interatomic potential for aluminum
title_sort	automated discovery of a robust interatomic potential for aluminum
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f563b56affa04410a543a78699a94661
work_keys_str_mv	AT justinssmith automateddiscoveryofarobustinteratomicpotentialforaluminum AT benjaminnebgen automateddiscoveryofarobustinteratomicpotentialforaluminum AT nithinmathew automateddiscoveryofarobustinteratomicpotentialforaluminum AT jiechen automateddiscoveryofarobustinteratomicpotentialforaluminum AT nicholaslubbers automateddiscoveryofarobustinteratomicpotentialforaluminum AT leonidburakovsky automateddiscoveryofarobustinteratomicpotentialforaluminum AT sergeitretiak automateddiscoveryofarobustinteratomicpotentialforaluminum AT haiahnam automateddiscoveryofarobustinteratomicpotentialforaluminum AT timothygermann automateddiscoveryofarobustinteratomicpotentialforaluminum AT saryufensin automateddiscoveryofarobustinteratomicpotentialforaluminum AT kiptonbarros automateddiscoveryofarobustinteratomicpotentialforaluminum
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Automated discovery of a robust interatomic potential for aluminum

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