Predicting Crystal Structures and Ionic Conductivities in Li$_{3}$YCl$_{6-x}$Br$_{x}$ Halide Solid Electrolytes Using a Fine-Tuned Machine Learning Interatomic Potential

Abstract

Understanding ionic transport in halide solid electrolytes is essential for advancing next-generation solid-state batteries. This work demonstrates the effectiveness of fine-tuning the Crystal Hamiltonian Graph Network (CHGNet) universal machine learning interatomic potential to accurately predict total energies, relaxed geometries, and lithium-ion dynamics in the ternary halide family Li$_{3}$YCl$_{6-x}$Br$_{x}$ (LYCB). Starting from experimentally refined disordered structures of Li$_{3}$YCl$_{6}$ and Li$_{3}$YBr$_{6}$, 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 LYCB, demonstrating the robustness of our approach for modeling transport properties in complex solid electrolytes.