Neural network potentials for reactive chemistry: CASPT2 quality potential energy surfaces for bond breaking-Reference-Cited by-同舟云学术

Neural network potentials for reactive chemistry: CASPT2 quality potential energy surfaces for bond breaking

Published:2023 Issue:4 Volume:2 Page:1058-1069
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Hu Quin H.¹^ORCID,Johannesen Andrew M.¹^ORCID,Graham Daniel S.¹²^ORCID,Goodpaster Jason D.¹^ORCID

Affiliation:

1. Department of Chemistry, University of Minnesota, 207 Pleasant St. SE, Minneapolis MN 55455, USA

2. Department of Chemistry and Physics, Birmingham-Southern College, 900 Arkadelphia Rd., Birmingham, AL 35254, USA

Abstract

Neural network potentials achieve CASPT2 accuracy for reactive chemistry and molecular simulations. Using transfer learning, these potentials require minimal CASPT2 data on small systems to accurately predict bond dissociation in larger systems.

Funder

Camille and Henry Dreyfus Foundation

National Science Foundation

Minnesota Supercomputing Institute, University of Minnesota

Office of Science

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D3DD00051F

Reference80 articles.

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