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

Autor(en)

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

Abstrakt

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.

Organisation(en)

Computergestützte Materialphysik

Externe Organisation(en)

Technische Universität Wien, Universität Wien, Wigner Research Centre for Physics, Charles University Prague, Università di Bologna

Journal

npj Computational Materials

Band

10

Anzahl der Seiten

9

ISSN

2096-5001

DOI

https://doi.org/10.1038/s41524-024-01289-4

Publikationsdatum

01-2024

Peer-reviewed

Ja

ÖFOS 2012

103009 Festkörperphysik

Schlagwörter

ASJC Scopus Sachgebiete

Mechanics of Materials, Allgemeine Materialwissenschaften, Computer Science Applications, Modelling and Simulation

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/1c46a1fe-bcab-4dfa-8cd7-2dfd25e717fb