氧气
材料科学
化学物理
结晶学
纳米技术
化学
有机化学
作者
Zheng Li,Ning Xu,Yujing Zhang,Wen Liu,Jiaqian Wang,Meiliang Ma,Xiaolan Fu,Xiaojuan Hu,Wenwu Xu,Zhongkang Han
标识
DOI:10.1021/acs.jpclett.4c00889
摘要
Understanding the structures of oxygen vacancies in bulk ceria is crucial as they significantly impact the material's catalytic and electronic properties. The complex interaction between oxygen vacancies and Ce3+ ions presents challenges in characterizing ceria's defect chemistry. We introduced a machine learning-assisted cluster-expansion model to predict the energetics of defective configurations accurately within bulk ceria. This model effectively samples configurational spaces, detailing oxygen vacancy structures across different temperatures and concentrations. At lower temperatures, vacancies tend to cluster, mediated by Ce3+ ions and electrostatic repulsion, while at higher temperatures, they distribute uniformly due to configurational entropy. Our analysis also reveals a correlation between thermodynamic stability and the band gap between occupied O 2p and unoccupied Ce 4f orbitals, with wider band gaps indicating higher stability. This work enhances our understanding of defect chemistry in oxide materials and lays the groundwork for further research into how these structural properties affect ceria's performance.
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