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Discovering Multi-Compositional Li-Argyrodite Solid-State Electrolytes via Experimental Active Learning
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AI
DOI：10.1002/smll.202410008.png)
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摘要
En 中文
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.
Keyword:
active learning
ambient-temperature ionic conductivity
argyrodite
multi-elemental doping
particle swarm optimization
期刊
IF:
12.1
论文数:
3.1W
被引数:
16.4W
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