skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

Authors:
; ;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1180197
Grant/Contract Number:
AC02-06CH11357
Resource Type:
Journal Article: Published Article
Journal Name:
Physical Review Letters
Additional Journal Information:
Journal Volume: 114; Journal Issue: 9; Journal ID: ISSN 0031-9007
Publisher:
American Physical Society
Country of Publication:
United States
Language:
English

Citation Formats

Li, Zhenwei, Kermode, James R., and De Vita, Alessandro. Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces. United States: N. p., 2015. Web. doi:10.1103/PhysRevLett.114.096405.
Li, Zhenwei, Kermode, James R., & De Vita, Alessandro. Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces. United States. doi:10.1103/PhysRevLett.114.096405.
Li, Zhenwei, Kermode, James R., and De Vita, Alessandro. Fri . "Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces". United States. doi:10.1103/PhysRevLett.114.096405.
@article{osti_1180197,
title = {Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces},
author = {Li, Zhenwei and Kermode, James R. and De Vita, Alessandro},
abstractNote = {},
doi = {10.1103/PhysRevLett.114.096405},
journal = {Physical Review Letters},
number = 9,
volume = 114,
place = {United States},
year = {Fri Mar 06 00:00:00 EST 2015},
month = {Fri Mar 06 00:00:00 EST 2015}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1103/PhysRevLett.114.096405

Citation Metrics:
Cited by: 54 works
Citation information provided by
Web of Science

Save / Share: