Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-10467
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dc.contributor.authorWestermayr, Julia-
dc.contributor.authorGastegger, Michael-
dc.contributor.authorMarquetand, Philipp-
dc.date.accessioned2020-08-19T10:01:13Z-
dc.date.available2020-08-19T10:01:13Z-
dc.date.issued2020-04-20-
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/11580-
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-10467-
dc.description.abstractIn recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advance the research field of photochemistry by learning all important properties—multiple energies, forces, and different couplings—for photodynamics simulations. We simplify such simulations substantially by (i) a phase-free training skipping costly preprocessing of raw quantum chemistry data; (ii) rotationally covariant nonadiabatic couplings, which can either be trained or (iii) alternatively be approximated from only ML potentials, their gradients, and Hessians; and (iv) incorporating spin–orbit couplings. As the deep-learning method, we employ SchNet with its automatically determined representation of molecular structures and extend it for multiple electronic states. In combination with the molecular dynamics program SHARC, our approach termed SchNarc is tested on two polyatomic molecules and paves the way toward efficient photodynamics simulations of complex systems.en
dc.description.sponsorshipEC/H2020/792572/EU/Machine Learning for Catalytic Carbon Dioxide Activation/MachineCaten
dc.description.sponsorshipEC/H2020/730897/EU/Transnational Access Programme for a Pan-European Network of HPC Research Infrastructures and Laboratories for scientific computing/HPC-EUROPA3en
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc530 Physikde
dc.subject.othercoupling reactionsen
dc.subject.otherquantum mechanicsen
dc.subject.otherhessiansen
dc.subject.othermolecular dynamics simulationsen
dc.subject.othermoleculesen
dc.titleCombining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamicsen
dc.typeArticleen
tub.accessrights.dnbfreeen
tub.publisher.universityorinstitutionTechnische Universität Berlinen
dc.identifier.eissn1948-7185-
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.1021/acs.jpclett.0c00527en
dcterms.bibliographicCitation.journaltitleJournal of Physical Chemistry Lettersen
dcterms.bibliographicCitation.originalpublisherplaceWashington, DCen
dcterms.bibliographicCitation.volume11en
dcterms.bibliographicCitation.pageend3834en
dcterms.bibliographicCitation.pagestart3828en
dcterms.bibliographicCitation.originalpublishernameAmerican Chemical Society (ACS)en
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