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*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
3 Downloads (Pure)

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
Number of pages7
JournalAdvanced Science
Volume7
Issue number15
Early online date1 Jan 2020
DOIs
Publication statusPublished - Aug 2020
MoE publication typeA1 Journal article-refereed

Keywords

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

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