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

ABSTRACT: 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 io

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC12589113 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature