Modelling Solid Electrolyte Materials Using Machine Learning Potentials

It is imperative to have highly conductive oxide ion and proton conductors that operate at intermediate temperatures to improve the performance of ceramic fuel cells. The crystal structures of these materials significantly influence their ionic conduction properties. Identifying novel materials with enhanced conductive abilities is a crucial research focus, promising substantial advancements in future fuel cell technologies.<br/> <br/>In pursuing this goal, we focus on advanced perovskite solid electrolytes and their interfaces. Employing atomistic simulations, we aim to unravel the complexities of these materials, gaining a detailed understanding of ion transport mechanisms crucial for optimizing electrolyte performance and efficiency. Our approach integrates the predictive capabilities of density functional theory with the dynamic nature of molecular dynamics, supplemented by machine-learning-based potentials for a thorough analysis.<br/> <br/>To address larger system sizes and extended time scales in our modelling, we utilize machine learning potentials, specifically moment tensor potentials (MTPs). The crucial potential fitting process relies on energies, forces, and stresses obtained from diverse configurations through ab initio calculations. Our study focuses on the systems Sr3V2O8 and Ba7Nb4MoO20, and our employed MTPs, fitted using active learning, exhibit excellent agreement with data from ab initio molecular dynamics (AIMD). Notably, oxygen and protons' diffusion coefficients, along with oxygen ions' diffusivity, show commendable consistency with both AIMD simulations and experimental data. This alignment underscores the accuracy and reliability of our MTPs in capturing the intricate dynamics of these materials, reinforcing their suitability for modelling and predicting the behaviour of Sr3V2O8 and Ba7Nb4MoO20.