Charge Transfer into Organic Thin Films: A Deeper Insight through Machine-Learning-Assisted Structure Search
dc.contributor | Aalto-yliopisto | fi |
dc.contributor | Aalto University | en |
dc.contributor.author | Egger, Alexander T. | en_US |
dc.contributor.author | Hörmann, Lukas | en_US |
dc.contributor.author | Jeindl, Andreas | en_US |
dc.contributor.author | Scherbela, Michael | en_US |
dc.contributor.author | Obersteiner, Veronika | en_US |
dc.contributor.author | Todorović, Milica | en_US |
dc.contributor.author | Rinke, Patrick | en_US |
dc.contributor.author | Hofmann, Oliver T. | en_US |
dc.contributor.department | Department of Applied Physics | en |
dc.contributor.groupauthor | Computational Electronic Structure Theory | en |
dc.contributor.organization | Graz University of Technology | en_US |
dc.date.accessioned | 2021-03-22T07:06:12Z | |
dc.date.available | 2021-03-22T07:06:12Z | |
dc.date.issued | 2020-08 | en_US |
dc.description.abstract | Density 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.version | Peer reviewed | en |
dc.format.extent | 7 | |
dc.format.mimetype | application/pdf | en_US |
dc.identifier.citation | Egger, 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.202000992 | en |
dc.identifier.doi | 10.1002/advs.202000992 | en_US |
dc.identifier.issn | 2198-3844 | |
dc.identifier.other | PURE UUID: 2e615905-278e-4092-b151-5b2b0399df60 | en_US |
dc.identifier.other | PURE ITEMURL: https://research.aalto.fi/en/publications/2e615905-278e-4092-b151-5b2b0399df60 | en_US |
dc.identifier.other | PURE LINK: http://www.scopus.com/inward/record.url?scp=85087172972&partnerID=8YFLogxK | en_US |
dc.identifier.other | PURE FILEURL: https://research.aalto.fi/files/56829374/Egger_Charge.advs.202000992.pdf | en_US |
dc.identifier.uri | https://aaltodoc.aalto.fi/handle/123456789/103172 | |
dc.identifier.urn | URN:NBN:fi:aalto-202103222450 | |
dc.language.iso | en | en |
dc.publisher | Wiley | |
dc.relation.ispartofseries | Advanced Science | en |
dc.relation.ispartofseries | Volume 7, issue 15 | en |
dc.rights | openAccess | en |
dc.subject.keyword | Bayesian inference | en_US |
dc.subject.keyword | charge transfer | en_US |
dc.subject.keyword | density functional theory | en_US |
dc.subject.keyword | hybrid interfaces | en_US |
dc.subject.keyword | machine learning | en_US |
dc.subject.keyword | organic electronics | en_US |
dc.subject.keyword | structure search | en_US |
dc.subject.keyword | vibrations | en_US |
dc.title | Charge Transfer into Organic Thin Films: A Deeper Insight through Machine-Learning-Assisted Structure Search | en |
dc.type | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä | fi |
dc.type.version | publishedVersion |