Deep learning-driven evaluation and prediction of ion-doped NASICON materials for enhanced solid-state battery performance

Abstract NASICON (Na $$_{1+x}$$ 1 + x Zr $$_2$$ 2 Si $$_x$$ x P $$_{3-x}$$ 3 - x O $$_{12}$$ 12 ) is a well-established solid-state electrolyte, renowned for its high ionic conductivity and excellent chemical stability, rendering it a promising candidate for solid-state batteries. However, the intri...

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Auteurs principaux:	Zirui Zhao, Xiaoke Wang, Si Wu, Pengfei Zhou, Qian Zhao, Guanping Xu, Kaitong Sun, Hai-Feng Li
Format:	Artigo
Langue:	Inglês
Publié:	Springer 2024-09-01
Collection:	AAPPS Bulletin
Sujets:	NASICON Solid-state electrolyte Ion doping Deep learning model Electrochemical properties
Accès en ligne:	https://doi.org/10.1007/s43673-024-00131-9
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