Active sparse Bayesian committee machine potential for isothermal–isobaric molecular dynamics simulations-Reference-Cited by-同舟云学术

Active sparse Bayesian committee machine potential for isothermal–isobaric molecular dynamics simulations

Published:2024 Issue:33 Volume:26 Page:22073-22082
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Willow Soohaeng Yoo¹^ORCID,Kim Dong Geon¹^ORCID,Sundheep R.¹,Hajibabaei Amir²^ORCID,Kim Kwang S.³^ORCID,Myung Chang Woo¹^ORCID

Affiliation:

1. Department of Energy Science, Sungkyunkwan University, Seobu-ro 2066, Suwon 16419, Korea

2. Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK

3. Center for Superfunctional Materials, Department of Chemistry, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea

Abstract

Introducing active sparse Bayesian committee machine potentials with virial kernels for enhanced pressure accuracy. This enables efficient on-the-fly training for accurate isobaric machine learning molecular dynamics simulations with reduced costs.

Funder

National Research Foundation of Korea

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/CP/D4CP01801J

Reference76 articles.

1. Machine Learning Force Fields

2. Gaussian Process Regression for Materials and Molecules

3. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

4. Data-Efficient Machine Learning Potentials from Transfer Learning of Periodic Correlated Electronic Structure Methods: Liquid Water at AFQMC, CCSD, and CCSD(T) Accuracy

5. J.Gilmer , S. S.Schoenholz , P. F.Riley , O.Vinyals and G. E.Dahl , Neural Message Passing for Quantum Chemistry , 2017