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Nonadiabatic Excited-State Dynamics with Machine Learning
[Image: see text] We show that machine learning (ML) can be used to accurately reproduce nonadiabatic excited-state dynamics with decoherence-corrected fewest switches surface hopping in a 1-D model system. We propose to use ML to significantly reduce the simulation time of realistic, high-dimension...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
American Chemical
Society
2018
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174422/ https://www.ncbi.nlm.nih.gov/pubmed/30200766 http://dx.doi.org/10.1021/acs.jpclett.8b02469 |