Discovering Multi‐Compositional Li‐Argyrodite Solid‐State Electrolytes via Experimental Active Learning (Small 21/2025)
Source abstract
Abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Materials and Technologies · Machine Learning in Materials Science · Solid-state spectroscopy and crystallography
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Affiliations
Nanotechnology & Advanced Materials Engineering Sejong University 209 Neungdong‐ro, Gwangjin‐gu Seoul 143‐747 South Korea
Department of Printed Electronics Sunchon National University 291‐19 Jungang‐ro Sunchon Chonnam 540‐742 South Korea
References
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