Elucidating oxide-ion and proton transport in ionic conductors using machine learning potentials.

The design and understanding of oxide-ion and proton transport in solid electrolytes are pivotal to the development of fuel cells that can operate at reduced temperatures of <600 ∘C. Atomistic modelling and machine learning are playing ever more crucial roles in achieving this objective. In this study, using passive and active learning techniques, we develop moment tensor potentials (MTPs) for two promising ionic conductors, namely, Ba7Nb4MoO20 and Sr3V2O8. Our MTPs accurately reproduce ab initio molecular dynamics data and demonstrate strong agreement with density functional theory calculations for forces, energies and stresses. They successfully predict diffusion coefficients and conductivities for both oxide ions and protons, showing excellent agreement with experimental data and ab initio molecular dynamics results. Additionally, the MTPs accurately estimate migration barriers, thereby underscoring their robustness and transferability. Our findings highlight the potential of MTPs in significantly reducing computational costs while maintaining high accuracy, making them invaluable for simulating complex ion transport mechanisms and supporting the development of next-generation solid oxide fuel cells.