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Machine learning exciton dynamics

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Preprint 01 Jan 2016Embargo end date: 01 Jan 2015 United States English Publisher:Royal Society of Chemistry (RSC)Journal:Chemical Science, volume 7, pages 5,139-5,147 (issn: 2041-6520, eissn: 2041-6539, Copyright policy )
Authors: Florian Häse; Stéphanie Valleau; Edward Pyzer-Knapp; Alán Aspuru-Guzik;

doi: 10.1039/c5sc04786b , 10.48550/arxiv.1511.07883

pmid: 30155164

pmc: PMC6020119

arXiv: 1511.07883

Machine learning exciton dynamics

Abstract

Machine learning ground state QM/MM for accelerated computation of exciton dynamics.

Country
United States
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Keywords

Chemical Physics (physics.chem-ph), FOS: Physical sciences, Computational Physics (physics.comp-ph), 540, 530, Chemistry, Physics - Chemical Physics, computational physics, Physics - Computational Physics

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 114
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    		 Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    		 Top 1%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
114
Top 1%
Top 10%
Top 1%
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gold
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