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Discovering Multi‐Compositional Li‐Argyrodite Solid‐State Electrolytes via Experimental Active Learning (Small 21/2025)
Author(s) -
Cho Min Young,
Pyo Kyunglim,
Lee Byung Do,
Kim Heejeong,
Shin Jiyoon,
Seo Jung Yong,
Park Woon Bae,
Sohn KeeSun
Publication year - 2025
Publication title -
small
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.785
H-Index - 236
eISSN - 1613-6829
pISSN - 1613-6810
DOI - 10.1002/smll.202570160
Solid‐State Electrolytes In article number 2410008, Woon Bae Park, Kee‐Sun Sohn, and co‐workers showcase the application of a particle swarm optimization algorithm, inspired by swarm intelligence, to design and optimize argyrodite solid‐state electrolytes (SSEs). The schematic highlights the collective behavior of particles navigating a complex solution space, fine‐tuning critical material parameters for enhanced ionic conductivity and stability. This innovative approach bridges computational intelligence with material science, streamlining the discovery and development of advanced SSEs for cutting‐edge energy storage technologies.