Issue 35, 2019

Machine learning enables long time scale molecular photodynamics simulations

Abstract

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.

Graphical abstract: Machine learning enables long time scale molecular photodynamics simulations

Supplementary files

Article information

Article type
Edge Article
Submitted
09 Apr. 2019
Accepted
02 Aug. 2019
First published
05 Aug. 2019
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2019,10, 8100-8107

Machine learning enables long time scale molecular photodynamics simulations

J. Westermayr, M. Gastegger, M. F. S. J. Menger, S. Mai, L. González and P. Marquetand, Chem. Sci., 2019, 10, 8100 DOI: 10.1039/C9SC01742A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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