Machine learning-accelerated discovery of multi-cation entropy-stabilized NASICON solid electrolytes with 10 000 hours of stable Na plating/stripping for all-solid-state sodium batteries

Energy & Environmental Science 2026 Open access

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Authors: Daxian Zuo, Jiaming Tian, Yu Sun, Xin Yu, Bo Peng, Tao Yu, Chengrong Xu, Xiang-Qun Xu, Yigang Wang, Yiwen Liu, Tianze Shi, Yinhui Feng
+3 more Jie Yang, Haoshen Zhou, Shaohua Guo
DOI: 10.1039/d5ee06594a
Cited by: 8
References: 59

Abstract

Machine learning (ML)-guided entropy engineering approach discovers a medium-entropy NASICON solid electrolyte (Na 3.5 Zr 1.0 Ti 0.5 Lu 0.5 Si 2 PO 12 ) with 1.3 mS cm −1 conductivity, 10 000 hours of Na cycling, and superior battery performance.

Fields of Study & Subjects

Advanced Battery Materials and Technologies Machine Learning in Materials Science Thermal Expansion and Ionic Conductivity

Publication details

Journal / source
Energy & Environmental Science
Publisher
Royal Society of Chemistry (RSC)
Volume
19
Issue
3
Pages
860–869
Publication date
1 January 2026
Language
EN