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 Li6.425Ge0.25Si0.375Sb0.375S4.8I1.2.