Decoding Lithium Metal Battery Degradation with Symmetric-Cell Artificial Intelligence Diagnostics (SAID).

Journal: Advanced materials (Deerfield Beach, Fla.)
Published Date: Nov 29, 2025

Abstract

While the underlying crosstalk effect between cathode and anode complicates the understanding of lithium metal degradation utilizing asymmetric (full)-cell data, employing a symmetric cell configuration enables isolation of contributions from specific electrodes. In this study, a symmetric-cell artificial intelligence diagnostics (SAID) is devised to decipher lithium metal anode degradation. Leveraging easily accessible, early-cycle lithium | lithium symmetric-cell data, SAID is demonstrated to accurately predict the elbow points (indicators of polarization acceleration) with a test mean absolute percentage error of 13.3%. More importantly, SAID reveals the persistent role of an initial-nucleation-related fingerprint in determining long-term cell polarization, which is validated through experiments and extended to full cells across different electrolytes. This approach, therefore, not only offers valuable insights into battery design but also exhibits great potential in uncovering hidden chemical correlations and advancing the field of energy storage in general.

Authors

  • Bo-Bo Zou
    Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China.
  • Kun-Yu Liu
    Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China.
  • Yu Yan
    School of Preclinical Medicine, Guangxi Medical University, No. 22, Shuangyong Road, Nanning, Guangxi 530021, China.
  • Xin-He Liu
    Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China.
  • Hong-Li Long
    Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China.
  • Meng-Yu Li
    Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China.
  • Hao-Bo Zhang
    Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China.
  • Kai-Xi You
    Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China.
  • Mi Chen
    Fuwai Yunnan Hospital, Chinese Academy of Medical Sciences, Affiliated Cardiovascular Hospital of Kunming Medical University, Kunming, China.
  • Xinyan Liu
    ICU, DongE Hospital Affiliated to Shandong First Medical University, Shandong, China.

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