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Predicting Crystal Structures and Ionic Conductivity in Li$_{3}$YCl$_{6-x}$Br$_{x}$ Halide Solid Electrolytes Using a Fine-Tuned Machine Learning Interatomic Potential
Jonas B\"ohm, Aur\'elie Champagne
https://arxiv.org/abs/2510.09861

Predicting Crystal Structures and Ionic Conductivity in Li$_{3}$YCl$_{6-x}$Br$_{x}$ Halide Solid Electrolytes Using a Fine-Tuned Machine Learning Interatomic Potential
This work demonstrates the effectiveness of fine-tuning the CHGNet universal machine learning interatomic potential (uMLIP) to investigate ionic transport mechanisms in ternary halide solid electrolytes of the Li$_{3}$YCl$_{6-x}$Br$_{x}$ family (x = 0 to 6), which are promising candidates for solid-state battery applications. We present a strategy for generating ordered structural models from experimentally derived disordered Li$_{3}$YCl$_{6}$ (LYC) and Li$_{3}$YBr$_{6}$ (LYB) structures. These…