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A Δ-machine learning approach for force fields, illustrated by a CCSD(T) 4-body correction to the MB-pol water potential
External Source
chorus
Document Type
Version of Record
Authors
Chen Qu (Independent Researcher, Toronto, Ontario M9B 0E3, Canada)
Qi Yu (Department of Chemistry, Yale University, New Haven, Connecticut 06520, USA)
Riccardo Conte (Dipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, 20133 Milano, Italy)
Paul L. Houston (Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, USA)
Apurba Nandi (Department of Chemistry, Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA)
Joel M. Bomwan (Department of Chemistry, Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA)
Date Acquired
February 10, 2024
Publication Date
January 1, 2022
Publication Information
Publication: Digital Discovery
Publisher: Royal Society of Chemistry (RSC)
Volume: 1
Issue: 5
e-ISSN: 2635-098X
DOI: 10.1039/d2dd00057a
Funding Number(s)
funding: W911NF-14-1-0471
funding: 80NSSC20K0360
funding: CHE-1954348
Distribution Limits
Public

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