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Machine learning enables long time scale molecular photodynamics simulations
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 bott...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Royal Society of Chemistry
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6849489/ https://www.ncbi.nlm.nih.gov/pubmed/31857878 http://dx.doi.org/10.1039/c9sc01742a |
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author | Westermayr, Julia Gastegger, Michael Menger, Maximilian F. S. J. Mai, Sebastian González, Leticia Marquetand, Philipp |
author_facet | Westermayr, Julia Gastegger, Michael Menger, Maximilian F. S. J. Mai, Sebastian González, Leticia Marquetand, Philipp |
author_sort | Westermayr, Julia |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6849489 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-68494892019-12-19 Machine learning enables long time scale molecular photodynamics simulations Westermayr, Julia Gastegger, Michael Menger, Maximilian F. S. J. Mai, Sebastian González, Leticia Marquetand, Philipp Chem Sci Chemistry 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. Royal Society of Chemistry 2019-08-05 /pmc/articles/PMC6849489/ /pubmed/31857878 http://dx.doi.org/10.1039/c9sc01742a Text en This journal is © The Royal Society of Chemistry 2019 http://creativecommons.org/licenses/by/3.0/ This article is freely available. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence (CC BY 3.0) |
spellingShingle | Chemistry Westermayr, Julia Gastegger, Michael Menger, Maximilian F. S. J. Mai, Sebastian González, Leticia Marquetand, Philipp Machine learning enables long time scale molecular photodynamics simulations |
title | Machine learning enables long time scale molecular photodynamics simulations
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title_full | Machine learning enables long time scale molecular photodynamics simulations
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title_fullStr | Machine learning enables long time scale molecular photodynamics simulations
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title_full_unstemmed | Machine learning enables long time scale molecular photodynamics simulations
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title_short | Machine learning enables long time scale molecular photodynamics simulations
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title_sort | machine learning enables long time scale molecular photodynamics simulations |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6849489/ https://www.ncbi.nlm.nih.gov/pubmed/31857878 http://dx.doi.org/10.1039/c9sc01742a |
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