DC FieldValueLanguage
dc.contributor.authorNeumann, Philipp-
dc.contributor.authorWittmer, Niklas-
dc.date.accessioned2022-04-11T05:47:42Z-
dc.date.available2022-04-11T05:47:42Z-
dc.date.issued2020-
dc.identifier.isbn9783030504328-
dc.identifier.issn03029743-
dc.description.abstractMolecular-continuum flow simulations combine molecular dynamics (MD) and computational fluid dynamics for multiscale considerations. A specific challenge in these simulations arises due to the “open MD boundaries” at the molecular-continuum interface: particles close to these boundaries do not feel any forces from outside which results in unphysical behavior and incorrect thermodynamic pressures. In this contribution, we apply neural networks to generate approximate boundary forces that reduce these artefacts. We train our neural network with force-distance pair values from periodic MD simulations and use this network to later predict boundary force contributions in non-periodic MD systems. We study different training strategies in terms of MD sampling and training for various thermodynamic state points and report on accuracy of the arising MD system. We further discuss computational efficiency of our approach in comparison to existing boundary force models.de_DE
dc.description.sponsorshipHigh Performance Computingde_DE
dc.language.isoende_DE
dc.relation.ispartofLecture notes in computer sciencede_DE
dc.subject.ddcDDC::000 Informatik, Informationswissenschaft, allgemeine Werkede_DE
dc.subject.ddcDDC::500 Naturwissenschaften und Mathematikde_DE
dc.titleOpen boundary modeling in molecular dynamics with machine learningde_DE
dc.typeConference Objectde_DE
dc.relation.conference20th International Conference on Computational Science ICCS 2020de_DE
dc.identifier.doi10.1007/978-3-030-50433-5_26-
dc.identifier.scopus2-s2.0-85087284627-
dcterms.bibliographicCitation.volume12142de_DE
dcterms.bibliographicCitation.pagestart334de_DE
dcterms.bibliographicCitation.pageend347de_DE
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85087284627-
local.submission.typeonly-metadatade_DE
dc.identifier.eissn16113349-
dc.type.conferenceObjectConference Paperde_DE
item.grantfulltextnone-
item.fulltext_sNo Fulltext-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
crisitem.author.deptHigh Performance Computing-
crisitem.author.parentorgFakultät für Maschinenbau und Bauingenieurwesen-
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