Discovering Multi‐Compositional Li‐Argyrodite Solid‐State Electrolytes via Experimental Active Learning (Small 21/2025) | Zendy

Cho Min Young | Zendy; Pyo Kyunglim | Zendy; Lee Byung Do | Zendy; Kim Heejeong | Zendy; Shin Jiyoon | Zendy; Seo Jung Yong | Zendy; Park Woon Bae | Zendy; Sohn KeeSun | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Premium

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.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here

Empowering knowledge with every search