Discovering virtual antiperovskites as solid-state electrolytes through active learning

Lee, Byung Do; Shin, Jiyoon; Kim, Seonghwan; Cho, Min Young; Lee, Young-Kook; Pyo, Myoungho; Park, Woon Bae; Sohn, Kee-Sun

doi:10.1016/j.ensm.2024.103535

Energy Storage Materials

2024

DOI: 10.1016/j.ensm.2024.103535

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Discovering virtual antiperovskites as solid-state electrolytes through active learning

Byung Do Lee,

Jiyoon Shin,

Seonghwan Kim

et al.

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Cited by 3 publications

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

Cho,

Pyo,

Lee

et al. 2024

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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.

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

Cho,

Pyo,

Lee

et al. 2024

Small

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Toward high performance all-solid-state lithium or sodium metal batteries: Potential application on Li/Na-rich antiperovskites (LiRAPs/NaRAPs) electrolyte for energy storage

Bi,

Mu,

Meng

et al. 2024

Energy Storage Materials

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Discovering virtual Na-based argyrodites as solid-state electrolytes using DFT, AIMD, and machine learning techniques

Lee,

Gavali,

Kim

et al. 2025

J. Mater. Chem. A

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Discovering virtual antiperovskites as solid-state electrolytes through active learning

Cited by 3 publications

References 88 publications

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

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

Toward high performance all-solid-state lithium or sodium metal batteries: Potential application on Li/Na-rich antiperovskites (LiRAPs/NaRAPs) electrolyte for energy storage

Discovering virtual Na-based argyrodites as solid-state electrolytes using DFT, AIMD, and machine learning techniques

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