Machine learning-based prediction of polaron-vacancy patterns on the TiO2(110) surface
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10492275" target="_blank" >RIV/00216208:11320/24:10492275 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=TcWFHJp3fu" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=TcWFHJp3fu</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41524-024-01289-4" target="_blank" >10.1038/s41524-024-01289-4</a>
Alternative languages
Result language
angličtina
Original language name
Machine learning-based prediction of polaron-vacancy patterns on the TiO2(110) surface
Original language description
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 TiO2(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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10305 - Fluids and plasma physics (including surface physics)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
npj Computational Materials
ISSN
2057-3960
e-ISSN
2057-3960
Volume of the periodical
10
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
9
Pages from-to
89
UT code for WoS article
001214855400001
EID of the result in the Scopus database
2-s2.0-85192167078