Article (Scientific journals)
Towards exact molecular dynamics simulations with machine-learned force fields
Chmiela, Stefan; Sauceda, Huziel E.; Müller, Klaus-Robert et al.
2018In Nature Communications, 9, p. 3887
Peer Reviewed verified by ORBi
 

Files


Full Text
133-Exact-MD-sGDML-NatureComms-2018.pdf
Publisher postprint (2.14 MB)
Download

All documents in ORBilu are protected by a user license.

Send to



Details



Abstract :
[en] Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in an automatic data-driven way. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy and allows converged molecular dynamics simulations with fully quantized electrons and nuclei. We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules. Our approach provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations.
Disciplines :
Physical, chemical, mathematical & earth Sciences: Multidisciplinary, general & others
Author, co-author :
Chmiela, Stefan
Sauceda, Huziel E.
Müller, Klaus-Robert
Tkatchenko, Alexandre ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Physics and Materials Science Research Unit
External co-authors :
yes
Language :
English
Title :
Towards exact molecular dynamics simulations with machine-learned force fields
Publication date :
15 October 2018
Journal title :
Nature Communications
ISSN :
2041-1723
Publisher :
Nature Publishing Group, London, United Kingdom
Volume :
9
Pages :
3887
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Computational Sciences
Physics and Materials Science
Available on ORBilu :
since 11 November 2018

Statistics


Number of views
329 (9 by Unilu)
Number of downloads
357 (6 by Unilu)

Scopus citations®
 
474
Scopus citations®
without self-citations
431
OpenCitations
 
336
WoS citations
 
450

Bibliography


Similar publications



Contact ORBilu