Machine Learning Based Prediction of Polaron-Vacancy Patterns on the TiO₂(110) Surface

Author(s)

Viktor C. Birschitzky, Igor Sokolovic, Michael Prezzi, Krisztian Palotas, Martin Setvin, Ulrike Diebold, Michele Reticcioli, Cesare Franchini

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$_{\rm O}$) 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$_{\rm O}$ distribution observed in scanning probe microscopy (SPM). Our innovative approach allows us to understand and predict defective surface patterns at previously inaccessible length scales, identifying the specific role of individual types of defects. Specifically, surface-polaron-stabilizing V$_{\rm O}$-configurations are identified, which could have consequences for surface reactivity.

Organisation(s)

Computational Materials Physics

Publication date

01-2024

Austrian Fields of Science 2012

103009 Solid state physics

Keywords

Portal url

https://ucris.univie.ac.at/portal/en/publications/machine-learning-based-prediction-of-polaronvacancy-patterns-on-the-tio2110-surface(1c46a1fe-bcab-4dfa-8cd7-2dfd25e717fb).html