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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...
Gespeichert in:
| Veröffentlicht in: | J Phys Chem Lett |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
American Chemical
Society
2018
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| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6174422/ https://ncbi.nlm.nih.gov/pubmed/30200766 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1021/acs.jpclett.8b02469 |
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