Charge Transfer into Organic Thin Films: A Deeper Insight through Machine-Learning-Assisted Structure Search

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorEgger, Alexander T.en_US
dc.contributor.authorHörmann, Lukasen_US
dc.contributor.authorJeindl, Andreasen_US
dc.contributor.authorScherbela, Michaelen_US
dc.contributor.authorObersteiner, Veronikaen_US
dc.contributor.authorTodorović, Milicaen_US
dc.contributor.authorRinke, Patricken_US
dc.contributor.authorHofmann, Oliver T.en_US
dc.contributor.departmentDepartment of Applied Physicsen
dc.contributor.groupauthorComputational Electronic Structure Theoryen
dc.contributor.organizationGraz University of Technologyen_US
dc.contributor.organizationHelsinki School of Economicsen_US
dc.date.accessioned2021-03-22T07:06:12Z
dc.date.available2021-03-22T07:06:12Z
dc.date.issued2020-08en_US
dc.description.abstractDensity functional theory calculations are combined with machine learning to investigate the coverage-dependent charge transfer at the tetracyanoethylene/Cu(111) hybrid organic/inorganic interface. The study finds two different monolayer phases, which exhibit a qualitatively different charge-transfer behavior. Our results refute previous theories of long-range charge transfer to molecules not in direct contact with the surface. Instead, they demonstrate that experimental evidence supports our hypothesis of a coverage-dependent structural reorientation of the first monolayer. Such phase transitions at interfaces may be more common than currently envisioned, beckoning a thorough reevaluation of organic/inorganic interfaces.en
dc.description.versionPeer revieweden
dc.format.extent7
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationEgger, A T, Hörmann, L, Jeindl, A, Scherbela, M, Obersteiner, V, Todorović, M, Rinke, P & Hofmann, O T 2020, ' Charge Transfer into Organic Thin Films : A Deeper Insight through Machine-Learning-Assisted Structure Search ', Advanced Science, vol. 7, no. 15, 2000992 . https://doi.org/10.1002/advs.202000992en
dc.identifier.doi10.1002/advs.202000992en_US
dc.identifier.issn2198-3844
dc.identifier.otherPURE UUID: 2e615905-278e-4092-b151-5b2b0399df60en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/2e615905-278e-4092-b151-5b2b0399df60en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85087172972&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/56829374/Egger_Charge.advs.202000992.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/103172
dc.identifier.urnURN:NBN:fi:aalto-202103222450
dc.language.isoenen
dc.publisherWiley
dc.relation.ispartofseriesAdvanced Scienceen
dc.relation.ispartofseriesVolume 7, issue 15en
dc.rightsopenAccessen
dc.subject.keywordBayesian inferenceen_US
dc.subject.keywordcharge transferen_US
dc.subject.keyworddensity functional theoryen_US
dc.subject.keywordhybrid interfacesen_US
dc.subject.keywordmachine learningen_US
dc.subject.keywordorganic electronicsen_US
dc.subject.keywordstructure searchen_US
dc.subject.keywordvibrationsen_US
dc.titleCharge Transfer into Organic Thin Films: A Deeper Insight through Machine-Learning-Assisted Structure Searchen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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