2025 AIChE Annual Meeting

(588q) Unraveling Superionic Transitions in Li3YCl6: Insights from Machine Learning Potentials and Van Der Waals Interactions

Authors

Wei-Fan Huang - Presenter, National Cheng Kung University
Mingjian Wen, University of Houston
Lars Grabow, University of Houston
Solid-state electrolytes (SSEs) are pivotal for advancing next-generation batteries due to their high energy density and enhanced safety profiles. Among SSEs, halide electrolytes such as LiYCl6 (LYC) are particularly promising, attributed to their low activation energy and high elastic modulus. This study leverages machine learning interatomic potentials within the Atomic Cluster Expansion (ACE) framework to comprehensively investigate the mechanical, thermal, and transport properties of LYC through extensive molecular simulations.

Halide electrolytes have garnered significant attention for their potential to overcome the limitations of conventional liquid electrolytes, including flammability and leakage. LYC, in particular, exhibits remarkable ionic conductivity and mechanical robustness, making it a strong candidate for solid-state battery applications.

Our simulations reveal that the LYC phase exhibits an ionic conductivity of 0.533 mS/cm at 300 K, with activation energies of 0.24 eV below, and 0.41 eV above the 425 K transition temperature. In contrast, the LYC phase demonstrates a higher ionic conductivity of 0.762 mS/cm at 300 K, with activation energies of 0.27 eV below, and 0.34 eV above 380 K. The computed elastic moduli (bulk, shear, Young’s) are 26.66, 15.51, and 38.98 GPa for LYC, and 25.32, 14.52, and 36.56 GPa for LYC.

A critical finding of this study is the indispensable role of the vdW-optB88 functional in accurately capturing long-range interactions, which is essential for predicting LYC’s transition temperatures. Without this functional, ionic conductivity calculations fail to reproduce the superionic transition. Additionally, we examine the impact of many-body dispersion energy with fractionally ionic model for polarizability on Li-ion transport properties. Future research will extend ACE-based modeling to LYC interfaces, focusing on lithium dendrite formation and interfacial reactions.