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

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2020-08
Major/Subject
Mcode
Degree programme
Language
en
Pages
7
Series
Advanced Science, Volume 7, issue 15
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.
Description
Keywords
Bayesian inference, charge transfer, density functional theory, hybrid interfaces, machine learning, organic electronics, structure search, vibrations
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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