Title: Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries
First author: Kai Li (Fudan University)
Corresponding author: Ying Wang (Fudan University)
In recent years, polymer solid-state electrolytes have received widespread attention in the field of high-energy-density lithium metal batteries. Various ion-conductive polymers with high electrical conductivity, electrochemical stability, and thermal stability have been developed. Among them, ionic liquids, as the basic components of ion-conductive polymer electrolytes, exhibit diverse types and complex selection criteria. Therefore, screening ionic liquids with high ion conductivity and a wide electrochemical window is crucial for achieving high safety and high energy density in lithium metal batteries.
Based on this, Ying Wang's team from Fudan University has developed a machine learning approach that combines quantum chemical calculations and graph convolutional neural networks. From a pool of 2220 ionic liquids generated by the cross-combination of different anions and cations from the IoLiTec website, they efficiently screened ionic liquids that are liquid at room temperature and simultaneously possess high ion conductivity and a wide chemical stability window. They prepared a series of thin (~50 µm) and robust (>200 MPa) ion-conductive polymer electrolytes. The experimental results reported in this work once again demonstrate the advantages and feasibility of using rigid rod-like polymer electrolytes for the fabrication of all-solid-state lithium metal batteries. Moreover, this work provides new insights and research directions for overcoming data scarcity issues and effectively utilizing machine learning in material optimization. The research was published in the top international journal Nature Communications, with Kai Li as the first author of the paper.
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