Machine learning drives a new paradigm in inorganic solid-state electrolytes research

The development of next-generation solid-state batteries (SSBs) relies heavily on the discovery of high-performance inorganic solid-state electrolytes (SSEs). However, conventional experimental and computational methods are often time-consuming and inefficient in exploring the vast compositional and...

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Detalles Bibliográficos

Principais autores:	Xiang Yin, Jun Ma, Shu Zhang, Guanglei Cui
Formato:	Artigo
Idioma:	Inglês
Publicado:	Elsevier 2026-06-01
Series:	Computational Materials Today
Assuntos:	Machine learning Solid-state electrolyte High-throughput screening Property prediction Inverse design All-solid-state batteries
Acceso en liña:	http://www.sciencedirect.com/science/article/pii/S2950463526000062
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