Titel
Machine learning-based prediction of polaron-vacancy patterns on the TiO2(110) surface
Autor*in
Igor Sokolović
Institute of Applied Physics, TU Wien
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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 (VO) and induced small polarons on rutile TiO2(110), effectively disentangling the interactions between polarons and defects. By combining neural-network supervised learning and simulated annealing, we elucidate the inhomogeneous VO 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 VO-configurations are identified, which could have consequences for surface reactivity.
Stichwort
Computational methodsElectronic structurePhotocatalysisSurfaces, interfaces and thin films
Objekt-Typ
Sprache
Englisch [eng]
Persistent identifier
Erschienen in
Titel
npj Computational Materials
Band
10
ISSN
2057-3960
Erscheinungsdatum
2024
Publication
Springer Science and Business Media LLC
Projekt
Erscheinungsdatum
2024
Zugänglichkeit
Rechteangabe
© The Author(s) 2024

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