Machine learning-based prediction of polaron-vacancy patterns on the TiO2(110) surface

Viktor C. Birschitzky (Corresponding author), Igor Sokolović, Michael Prezzi, Krisztián Palotás, Martin Setvín, Ulrike Diebold, Michele Reticcioli, Cesare Franchini

Publications: Contribution to journalArticlePeer Reviewed

Abstract

The multifaceted physics of oxides is shaped by their composition and the presence of defects, which are often accompanied by the formation of polarons. The simultaneous presence of polarons and defects, and their complex interactions, pose challenges for first-principles simulations and experimental techniques. In this study, we leverage machine learning and a first-principles database to analyze the distribution of surface oxygen vacancies (V O) and induced small polarons on rutile TiO 2(110), effectively disentangling the interactions between polarons and defects. By combining neural-network supervised learning and simulated annealing, we elucidate the inhomogeneous V O distribution observed in scanning probe microscopy (SPM). Our approach allows us to understand and predict defective surface patterns at enhanced length scales, identifying the specific role of individual types of defects. Specifically, surface-polaron-stabilizing V O-configurations are identified, which could have consequences for surface reactivity.

Original languageEnglish
Article number89
Number of pages9
Journalnpj Computational Materials
Volume10
Issue number1
Early online date22 Jan 2024
DOIs
Publication statusPublished - 6 May 2024

Austrian Fields of Science 2012

  • 103009 Solid state physics

Keywords

  • cond-mat.mtrl-sci

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