Facilitating <i>ab initio</i> QM/MM free energy simulations by Gaussian process regression with derivative observations-Reference-Cited by-同舟云学术

Facilitating ab initio QM/MM free energy simulations by Gaussian process regression with derivative observations

Published:2022 Issue:41 Volume:24 Page:25134-25143
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Snyder Ryan¹,Kim Bryant¹,Pan Xiaoliang²^ORCID,Shao Yihan²^ORCID,Pu Jingzhi¹^ORCID

Affiliation:

1. Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, 402 N. Blackford St., Indianapolis, IN 46202, USA

2. Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Pkwy, Norman, OK 73019, USA

Abstract

In this machine-learning-facilitated method, Gaussian process regression (GPR) is used to predict energy and force corrections for a semiempirical QM/MM level to match with ab initio QM/MM results during MD-based free energy simulations.

Funder

Indiana University-Purdue University Indianapolis

National Institutes of Health

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2022/CP/D2CP02820D

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