Machine learning enables long time scale molecular photodynamics simulations-Reference-Cited by-同舟云学术

Machine learning enables long time scale molecular photodynamics simulations

Published:2019 Issue:35 Volume:10 Page:8100-8107
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Westermayr Julia¹²³⁴⁵^ORCID,Gastegger Michael⁶⁷⁸⁹^ORCID,Menger Maximilian F. S. J.¹²³⁴⁵,Mai Sebastian¹²³⁴⁵^ORCID,González Leticia¹²³⁴⁵^ORCID,Marquetand Philipp¹²³⁴⁵^ORCID

Affiliation:

1. Institute of Theoretical Chemistry

2. Faculty of Chemistry

3. University of Vienna

4. 1090 Vienna

5. Austria

6. Machine Learning Group

7. Technical University of Berlin

8. 10587 Berlin

9. Germany

Abstract

Machine learning enables excited-state molecular dynamics simulations including nonadiabatic couplings on nanosecond time scales.

Funder

H2020 Marie Skłodowska-Curie Actions

Austrian Science Fund

Universität Wien

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2019/SC/C9SC01742A

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