Stable and accurate atomistic simulations of flexible molecules using conformationally generalisable machine learned potentials-Reference-Cited by-同舟云学术

Stable and accurate atomistic simulations of flexible molecules using conformationally generalisable machine learned potentials

Published:2024 Issue:32 Volume:15 Page:12780-12795
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Williams Christopher D.¹^ORCID,Kalayan Jas²^ORCID,Burton Neil A.³^ORCID,Bryce Richard A.¹^ORCID

Affiliation:

1. Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK

2. Science and Technologies Facilities Council (STFC), Daresbury Laboratory, Keckwick Lane, Daresbury, Warrington, WA4 4AD, UK

3. Department of Chemistry, School of Natural Sciences, Faculty of Science and Engineering, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK

Abstract

We present a strategy for generating global machine learned potentials capable of accurate, fast and stable atomistic simulations of flexible molecules. Key to stability is training datasets that contain all conformers of the target molecule.

Funder

Engineering and Physical Sciences Research Council

Leverhulme Trust

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/SC/D4SC01109K

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