A machine learning-assisted study of the formation of oxygen vacancies in anatase titanium dioxide.

ABSTRACT: Defect engineering of semiconductor photocatalysts is critical in reducing the reaction barriers. The generation of surface oxygen vacancies allows substantial tuning of the electronic structure of anatase titanium dioxide (TiO₂), but disclosing the vacancy formation at the atomic level remains complex or time-consuming. Herein, we combine density functional theory calculations with machine learning to identify the main factors affecting the formation of oxygen defects and accelerate the prediction of vacancy formation. The results show that the first two-layer oxygen atoms on the typical surfaces of TiO_2, including (100), (110), and (211) facets, are more likely to be activated when the gas is more reduced, the pressure is higher, and the reduction temperature is inc

SUBMITTER: Wang D 

PROVIDER: S-EPMC11494461 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature