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Issue 35, 2019
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Machine learning enables long time scale molecular photodynamics simulations

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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

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Publication details

The article was received on 09 Apr 2019, accepted on 02 Aug 2019 and first published on 05 Aug 2019


Article type: Edge Article
DOI: 10.1039/C9SC01742A
Chem. Sci., 2019,10, 8100-8107
  • Open access: Creative Commons BY license
    All publication charges for this article have been paid for by the Royal Society of Chemistry

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    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

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