Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Computational modelling of chemical systems requires a balance between accuracy and computational cost. Here the authors use transfer learning to develop a general purpose neural network potential that approaches quantum-chemical accuracy for reaction thermochemistry, isomerization, and drug-like mo...

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Main Authors: Justin S. Smith, Benjamin T. Nebgen, Roman Zubatyuk, Nicholas Lubbers, Christian Devereux, Kipton Barros, Sergei Tretiak, Olexandr Isayev, Adrian E. Roitberg
Format: article
Language:EN
Published: Nature Portfolio 2019
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Online Access:https://doaj.org/article/b9d6f6a078d94ad99b62149bfbdabce9
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spelling oai:doaj.org-article:b9d6f6a078d94ad99b62149bfbdabce92021-12-02T16:58:01ZApproaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning10.1038/s41467-019-10827-42041-1723https://doaj.org/article/b9d6f6a078d94ad99b62149bfbdabce92019-07-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-10827-4https://doaj.org/toc/2041-1723Computational modelling of chemical systems requires a balance between accuracy and computational cost. Here the authors use transfer learning to develop a general purpose neural network potential that approaches quantum-chemical accuracy for reaction thermochemistry, isomerization, and drug-like molecular torsions.Justin S. SmithBenjamin T. NebgenRoman ZubatyukNicholas LubbersChristian DevereuxKipton BarrosSergei TretiakOlexandr IsayevAdrian E. RoitbergNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-8 (2019)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
Justin S. Smith
Benjamin T. Nebgen
Roman Zubatyuk
Nicholas Lubbers
Christian Devereux
Kipton Barros
Sergei Tretiak
Olexandr Isayev
Adrian E. Roitberg
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
description Computational modelling of chemical systems requires a balance between accuracy and computational cost. Here the authors use transfer learning to develop a general purpose neural network potential that approaches quantum-chemical accuracy for reaction thermochemistry, isomerization, and drug-like molecular torsions.
format article
author Justin S. Smith
Benjamin T. Nebgen
Roman Zubatyuk
Nicholas Lubbers
Christian Devereux
Kipton Barros
Sergei Tretiak
Olexandr Isayev
Adrian E. Roitberg
author_facet Justin S. Smith
Benjamin T. Nebgen
Roman Zubatyuk
Nicholas Lubbers
Christian Devereux
Kipton Barros
Sergei Tretiak
Olexandr Isayev
Adrian E. Roitberg
author_sort Justin S. Smith
title Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
title_short Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
title_full Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
title_fullStr Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
title_full_unstemmed Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
title_sort approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
publisher Nature Portfolio
publishDate 2019
url https://doaj.org/article/b9d6f6a078d94ad99b62149bfbdabce9
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