A Database of Composition‐Processing‐Performance Parameters for over 400 Inorganic‐Polymer Composite Solid‐State Electrolytes

Abstract

Inorganic‐polymer composite solid‐state electrolytes (IPCSEs), which combine the advantages of inorganic fillers and polymer matrices, have emerged as promising candidates for all‐solid‐state batteries. However, achieving high ionic conductivity at room‐temperature remains challenging due to interfacial phase effects, percolation‐threshold limitations, and processing‐induced structural defects. Moreover, the fragmentation and heterogeneity of existing literature data complicates systematic optimization, necessitating a unified database for data‐driven discovery. Here, a comprehensive and traceable database is constructed by extracting and consolidating data from peer‐reviewed literature, encompassing material compositions, processing conditions, and electrolyte performance for over 400 IPCSEs. Through Pearson correlation analysis, which quantifies a linear relationship between variables, key factors influencing ionic conductivity are identified, including filler type, content, and morphology. To validate the database's utility, a machine‐learning‐ready dataset is constructed and tassorted predictive models are trained. Experimental results show that the ionic conductivity prediction performance of support vector regression reaches an R2 of 0.90, demonstrating high‐quality of the dataset and the promising utility for design optimization and quantitative assessment of composition‐processing‐performance relationships. This work not only offers a structural database for artificial‐intelligence‐driven electrolyte development but also translates data‐driven insights into practical tools for advancing solid‐state battery materials.