Designing and understanding light-harvesting devices with machine learning

Photon-induced charge separation phenomena are at the heart of light-harvesting applications but challenging to be described by quantum mechanical models. Here the authors illustrate the potential of machine-learning approaches towards understanding the fundamental processes governing electronic exc...

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Autores principales:	Florian Häse, Loïc M. Roch, Pascal Friederich, Alán Aspuru-Guzik
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/8e95f0aafa4d4b12ab2e077e5b7f3b6f
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spelling	oai:doaj.org-article:8e95f0aafa4d4b12ab2e077e5b7f3b6f2021-12-02T14:54:28ZDesigning and understanding light-harvesting devices with machine learning10.1038/s41467-020-17995-82041-1723https://doaj.org/article/8e95f0aafa4d4b12ab2e077e5b7f3b6f2020-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-17995-8https://doaj.org/toc/2041-1723Photon-induced charge separation phenomena are at the heart of light-harvesting applications but challenging to be described by quantum mechanical models. Here the authors illustrate the potential of machine-learning approaches towards understanding the fundamental processes governing electronic excitations.Florian HäseLoïc M. RochPascal FriederichAlán Aspuru-GuzikNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Florian Häse Loïc M. Roch Pascal Friederich Alán Aspuru-Guzik Designing and understanding light-harvesting devices with machine learning
description	Photon-induced charge separation phenomena are at the heart of light-harvesting applications but challenging to be described by quantum mechanical models. Here the authors illustrate the potential of machine-learning approaches towards understanding the fundamental processes governing electronic excitations.
format	article
author	Florian Häse Loïc M. Roch Pascal Friederich Alán Aspuru-Guzik
author_facet	Florian Häse Loïc M. Roch Pascal Friederich Alán Aspuru-Guzik
author_sort	Florian Häse
title	Designing and understanding light-harvesting devices with machine learning
title_short	Designing and understanding light-harvesting devices with machine learning
title_full	Designing and understanding light-harvesting devices with machine learning
title_fullStr	Designing and understanding light-harvesting devices with machine learning
title_full_unstemmed	Designing and understanding light-harvesting devices with machine learning
title_sort	designing and understanding light-harvesting devices with machine learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/8e95f0aafa4d4b12ab2e077e5b7f3b6f
work_keys_str_mv	AT florianhase designingandunderstandinglightharvestingdeviceswithmachinelearning AT loicmroch designingandunderstandinglightharvestingdeviceswithmachinelearning AT pascalfriederich designingandunderstandinglightharvestingdeviceswithmachinelearning AT alanaspuruguzik designingandunderstandinglightharvestingdeviceswithmachinelearning
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Designing and understanding light-harvesting devices with machine learning

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