A Theoretical Study of Oxygen Anion Diffusion through the A-Site Deficient SrTiO₃ Lattice Structure: A Machine Learning Approach

Abstract

The performance of strontium titanate-based perovskite materials, widely employed as electrode materials for reversible solid oxide cells, is directly characterized by their efficiency and their ability to facilitate the diffusion of generated oxygen ions. A technique frequently employed for enhancing oxygen ion diffusivity involves artificially generating A-site vacancies in these structures. In this study, the molecular-level mechanism of oxygen ion diffusion for a range of A-site deficient structures is extensively investigated using combined molecular dynamics simulations and machine learning-based technique. The analysis of molecular simulation trajectories yields diffusion parameters for the bulk system. Additionally, clustering analysis of time-overlapped locations of oxygen ions provides a spatial distribution of oxygen ion dislocations. Concurrently, tracking the motion of individual oxygen ions elucidates the contribution of each ion to the overall ionic conductance. Overall, the systematic generation of A-site deficiency is found to significantly influence oxygen ion dislocations, thereby impacting the performance of these materials in terms of oxide ion conduction.