A data-mining approach to understanding the impact of multi-doping on the ionic transport mechanism of solid electrolytes materials: the case of dual-doped Ga0.15/Scy Li7La3Zr2O12

Abstract

This study presents novel computational methods applied to the technologically significant solid electrolyte materials, Li_6.55+yGa_0.15La₃Zr_2−ySc_yO₁₂ (Ga_0.15/Sc_y-LLZO), in order to investigate the effect of the distribution of Ga³⁺ on Li-ion dynamics. Utilizing a specifically designed first-principles-based force field, molecular dynamics, and advanced hybrid Monte Carlo simulations, we systematically examine the material's transport properties for a range of Ga³⁺ and Sc³⁺ cationic concentrations. Additionally, we introduce innovative post-processing methods employing data mining clustering techniques, shedding light on Li⁺ ion behavior and conductivity mechanisms. Contrary to prior assumptions, the presence of Ga³⁺ in octahedral sites, not tetrahedral junctions, optimally enhances Li-ion conductivity, unlocking Li-ion diffusion pathways. The research illuminates how dopant distribution influences Li⁺ site occupancy and conductivity, offering key insights for advanced solid electrolyte design.