Résumé
Understanding ionic transport in halide solid electrolytes (SEs) is essential for advancing next-generation solid-state batteries. This work demonstrates the effectiveness of fine-tuning the Crystal Hamiltonian Graph Network universal machine learning interatomic potential to accurately predict total energies, relaxed geometries, and lithium-ion dynamics in the ternary halide family Li3YCl6-xBrx. Starting from experimentally refined disordered structures of Li3YCl6 and Li3YBr6, we present a strategy for generating ordered structural models through systematic enumeration and energy ranking, providing realistic structural models. These serve as initial configurations for an iterative fine-tuning workflow that integrates molecular dynamics simulations and static density functional theory calculations to achieve near-ab initio accuracy at four orders of magnitude lower computational cost. We further reveal the influence of composition (varied x) on the predicted phase stability and ionic conductivity in Li3YCl6-xBrx, demonstrating the robustness of our approach for modeling transport properties in complex SEs.