Recently, the research team headed by Renowned Researcher Liu Xinyan from the Institute of Fundamental and Frontier Sciences at the University of Electronic Science and Technology of China, had a research paper entitled "Decoding Lithium Metal Battery Degradation with Symmetric-cell Artificial Intelligence Diagnostics (SAID)" published in the prestigious journal Advanced Materials within the realm of materials science.

This study tackles the fundamental challenge of striking a balance between interface stability and ion transport efficiency in lithium metal batteries (LMBs) as their energy density escalates. It puts forward a symmetric-cell-based artificial intelligence diagnostic framework (SAID). By building a multi-scale data fusion model that combines electrochemical impedance spectroscopy, in-situ optical microscopy, and machine learning algorithms, the research team accomplished, for the very first time, the real-time tracking and quantitative analysis of interface side reactions in lithium metal batteries.

The experimental outcomes reveal that the SAID framework can precisely pinpoint the spatial distribution traits of electrolyte decomposition products on the electrode surface, unveiling the dynamic evolution patterns of inorganic-enriched solid electrolyte interfaces (SEIs). This accomplishment lays a solid theoretical foundation for optimizing the design of electrolyte formulations in high-energy-density lithium metal batteries. Already, three national invention patents have been filed for the related technologies.

Postdoctoral researcher Zou Bobo and the 2024 doctoral candidate Liu Kunyu from the Institute of Fundamental and Frontier Sciences acted as the co-first authors of the paper, while Renowned Researcher Liu Xinyan served as the corresponding author. The University of Electronic Science and Technology of China stands as the sole institution responsible for completing this research.