期刊
CHEMICAL SCIENCE
卷 10, 期 35, 页码 8100-8107出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/c9sc01742a
关键词
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资金
- uni:docs program of the University of Vienna
- European Union Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant [792572]
- ITN-EJD project [642294]
- Austrian Science Fund [I 2883]
- Marie Curie Actions (MSCA) [792572] Funding Source: Marie Curie Actions (MSCA)
- Austrian Science Fund (FWF) [I2883] Funding Source: Austrian Science Fund (FWF)
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.
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