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

  • Alexander T. Egger
  • , Lukas Hörmann
  • , Andreas Jeindl
  • , Michael Scherbela
  • , Veronika Obersteiner
  • , Milica Todorović
  • , Patrick Rinke
  • , Oliver T. Hofmann

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

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.

Original languageEnglish
Article number2000992
JournalAdvanced Science
Volume7
Issue number15
DOIs
StatePublished - 1 Aug 2020
Externally publishedYes

Keywords

  • Bayesian inference
  • charge transfer
  • density functional theory
  • hybrid interfaces
  • machine learning
  • organic electronics
  • structure search
  • vibrations

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