Discovering Multi‐Compositional Li‐Argyrodite Solid‐State Electrolytes via Experimental Active Learning

Abstract Significant research has focused on doping third‐party elements into representative Li‐Argyrodites, which typically consist of a metal cation, a sulfide anion, and a halide. These efforts have generally been limited to doping or substituting a single element at each atomic site in the Argyrodite structure, resulting in, at most, binary combinations at each site. Multi‐elemental doping or substitution poses a challenge due to the so‐called combinatorial explosion issue. Here, the study reports quaternary and ternary combinations at either the cation or anion sites, optimizing the composition for ambient‐temperature ionic conductivity. Managing such a complex multi‐compositional system requires artificial intelligence that surpasses human intuition. A particle swarm optimization (PSO) algorithm is employed within an active learning framework to tackle this multi‐dimensional optimization problem. Unlike typical active learning approaches that rely on theoretical computational data, the process is driven by experimental data from the synthesis and characterization of a few hundred multi‐compositional Argyrodite samples This experimental active learning approach ultimately enables identifying a novel multi‐compositional Li‐Argyrodite, exhibiting ambient‐temperature ionic conductivity of 13.02 mS cm⁻¹ and enhanced cell performance, with the composition Li_6.425Ge_0.25Si_0.375Sb_0.375S_4.8I_1.2.