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

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
DOIs
Publication statusE-pub ahead of print - 1 Jan 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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  • Projects

    Artificial Intelligence for Microscopic Structure Search

    Ghosh, K., Himanen, L., Rinke, P., Geurts, A., Li, J. & Todorovic, M.

    01/01/201831/12/2021

    Project: Academy of Finland: Other research funding

    COMP: Centre of Excellence in Computational Nanoscience

    Järvi, J., Li, J., Todorovic, M., Rinke, P. & Dvorak, M.

    01/01/201631/12/2017

    Project: Academy of Finland: Other research funding

    Equipment

    Science-IT

    Mikko Hakala (Manager)

    School of Science

    Facility/equipment: Facility

  • Press / Media

    Machine learning methods provide new insights into organic-inorganic interfaces

    Patrick Rinke

    04/08/2020

    3 items of Media coverage

    Press/Media: Media appearance

    Cite this

    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, [2000992]. https://doi.org/10.1002/advs.202000992