Machine learning enables long time scale molecular photodynamics simulations.
Journal
Chemical science
ISSN: 2041-6520
Titre abrégé: Chem Sci
Pays: England
ID NLM: 101545951
Informations de publication
Date de publication:
21 Sep 2019
21 Sep 2019
Historique:
received:
09
04
2019
accepted:
02
08
2019
entrez:
21
12
2019
pubmed:
21
12
2019
medline:
21
12
2019
Statut:
epublish
Résumé
Photo-induced processes are fundamental in nature but accurate simulations of their dynamics are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales. Here we introduce a method based on machine learning to overcome this bottleneck and enable accurate photodynamics on nanosecond time scales, which are otherwise out of reach with contemporary approaches. Instead of expensive quantum chemistry during molecular dynamics simulations, we use deep neural networks to learn the relationship between a molecular geometry and its high-dimensional electronic properties. As an example, the time evolution of the methylenimmonium cation for one nanosecond is used to demonstrate that machine learning algorithms can outperform standard excited-state molecular dynamics approaches in their computational efficiency while delivering the same accuracy.
Identifiants
pubmed: 31857878
doi: 10.1039/c9sc01742a
pii: c9sc01742a
pmc: PMC6849489
doi:
Types de publication
Journal Article
Langues
eng
Pagination
8100-8107Subventions
Organisme : Austrian Science Fund FWF
ID : W 1232
Pays : Austria
Informations de copyright
This journal is © The Royal Society of Chemistry 2019.
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