Neural network interatomic potential for laser-excited materials

dc.date.accessioned2023-12-18T11:41:26Z
dc.date.available2023-12-18T11:41:26Z
dc.date.issued2023-08-15
dc.description.sponsorshipGefördert im Rahmen des Projekts DEAL
dc.identifierdoi:10.17170/kobra-202312189239
dc.identifier.urihttp://hdl.handle.net/123456789/15308
dc.language.isoeng
dc.relation.doidoi:10.1038/s43246-023-00389-w
dc.rightsNamensnennung 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectNeuronale Netzeger
dc.subjectDatengesteuerte interatomare Potentialeger
dc.subjectLaser-erregte Materialienger
dc.subject.ddc620
dc.subject.swdNeuronales Netzger
dc.subject.swdMaschinelles Lernenger
dc.subject.swdLaserger
dc.subject.swdErregungger
dc.subject.swdMaterialger
dc.titleNeural network interatomic potential for laser-excited materialseng
dc.typeAufsatz
dc.type.versionpublishedVersion
dcterms.abstractData-driven interatomic potentials based on machine-learning approaches have been increasingly used to perform large-scale, first-principles quality simulations of materials in the electronic ground state. However, they are not able to describe situations in which the electrons are excited, like in the case of material processing by means of femtosecond laser irradiation or ion bombardment. In this work, we propose a neural network interatomic potential with an explicit dependency on the electronic temperature. Taking silicon as an example, we demonstrate its capability of reproducing important physical properties with first-principles accuracy and use it to simulate laser-induced surface modifications on a thin film at time and length scales that are impossible to reach with first-principles approaches like density functional theory. The method is general and can be applied not only to other laser-excited materials but also to condensed and liquid matter under non-equilibrium situations in which electrons and ions exhibit different temperatures.eng
dcterms.accessRightsopen access
dcterms.creatorPlettenberg, Pascal
dcterms.creatorBauerhenne, Bernd
dcterms.creatorGarcia, Martin Ezequiel
dcterms.source.articlenumber63
dcterms.source.identifiereissn:2662-4443
dcterms.source.journalCommunications Materialseng
dcterms.source.volumeVolume 4
kup.iskupfalse

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