Open Access Article
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Correction: A Δ-machine learning approach for force fields, illustrated by a CCSD(T) 4-body correction to the MB-pol water potential
Chen
Qu
*a,
Qi
Yu
b,
Riccardo
Conte
c,
Paul L.
Houston
de,
Apurba
Nandi
f and
Joel M.
Bowman
*f
aIndependent Researcher, Toronto, Ontario M9B 0E3, Canada. E-mail: szquchen@gmail.com
bDepartment of Chemistry, Yale University, New Haven, Connecticut 06520, USA
cDipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, 20133 Milano, Italy
dDepartment of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, USA
eDepartment of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
fDepartment of Chemistry, Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA. E-mail: jmbowma@emory.edu
First published on 25th October 2022
Abstract
Correction for ‘A Δ-machine learning approach for force fields, illustrated by a CCSD(T) 4-body correction to the MB-pol water potential’ by Chen Qu et al., Digital Discovery, 2022, 1, 658–664, https://doi.org/10.1039/D2DD00057A.
Joel M. Bowman’s name was spelled incorrectly in the original version of this article. The correct spelling can be found in the author list of this correction.
The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.
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