A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Charge Transfer into Organic Thin Films: A Deeper Insight through Machine-Learning-Assisted Structure Search
Tekijät: Egger AT, Hormann L, Jeindl A, Scherbela M, Obersteiner V, Todorovic M, Rinke P, Hofmann OT
Kustantaja: WILEY
Julkaisuvuosi: 2020
Lehti:Advanced Science
Tietokannassa oleva lehden nimiADVANCED SCIENCE
Lehden akronyymi: ADV SCI
Artikkelin numero: 2000992
Vuosikerta: 7
Numero: 15
Sivujen määrä: 7
DOI: https://doi.org/10.1002/advs.202000992
Tiivistelmä
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.
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.
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