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

Alexander Egger; Lukas Hörmann; Andreas Jeindl; Michael Scherbela; Veronika Obersteiner; Milica Todorović; Patrick Rinke; Oliver Hofmann

doi:10.1002/advs.202000992

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

Alexander Egger, Lukas Hörmann, Andreas Jeindl, Michael Scherbela, Veronika Obersteiner, Milica Todorović, Patrick Rinke, Oliver Hofmann^*

^*Corresponding author for this work

Institute of Solid State Physics (5130)

Research output: Contribution to journal › Article › peer-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 language	English
Article number	2000992
Journal	Advanced Science
Volume	7
Issue number	15
DOIs	https://doi.org/10.1002/advs.202000992
Publication status	Published - 28 Jun 2020

Keywords

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

ASJC Scopus subject areas

General Engineering
General Physics and Astronomy
General Chemical Engineering
Biochemistry, Genetics and Molecular Biology (miscellaneous)
General Materials Science
Medicine (miscellaneous)

Fields of Expertise

Advanced Materials Science

Cooperations

NAWI Graz

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10.1002/advs.202000992Licence: CC BY 4.0

Cite this

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title = "Charge Transfer into Organic Thin Films: A Deeper Insight through Machine‐Learning‐Assisted Structure Search",

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.",

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T1 - Charge Transfer into Organic Thin Films: A Deeper Insight through Machine‐Learning‐Assisted Structure Search

AU - Egger, Alexander

AU - Hörmann, Lukas

AU - Jeindl, Andreas

AU - Scherbela, Michael

AU - Obersteiner, Veronika

AU - Todorović, Milica

AU - Rinke, Patrick

AU - Hofmann, Oliver

PY - 2020/6/28

Y1 - 2020/6/28

N2 - 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.

AB - 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.

KW - Bayesian inference

KW - charge transfer

KW - density functional theory

KW - hybrid interfaces

KW - machine learning

KW - organic electronics

KW - structure search

KW - vibrations

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U2 - 10.1002/advs.202000992

DO - 10.1002/advs.202000992

M3 - Article

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VL - 7

JO - Advanced Science

JF - Advanced Science

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M1 - 2000992

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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Cooperations

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