Uni‐Electrolyte: An Artificial Intelligence Platform for Designing Electrolyte Molecules for Rechargeable Batteries
Xiang Chen, Mingkang Liu, Shiqiu Yin, Yuchen Gao, Nan Yao, Qiang Zhang
Abstract
Abstract Electrolytes are an essential part of rechargeable batteries, such as lithium batteries. However, electrolyte innovation is facing grand challenges due to the complicated solution chemistry and infinite molecular space (>10 60 for small molecules). This work reported an artificial intelligence (AI) platform, namely Uni‐Electrolyte, for designing advanced electrolyte molecules, which mainly includes three parts, i.e., EMolCurator, EMolForger, and EMolNetKnittor. New molecules can be designed by combining high‐throughput screening and generative AI models from more than 100 million alternative molecules in the EMolCurator module. The molecular properties, including frontier molecular orbital information, formation energy, binding energy with a Li ion, viscosity, and dielectric constant, can be adopted as the screening parameters. The EMolForger and EMolNetKnittor modules can predict the retrosynthesis pathway and solid electrolyte interphase (SEI) formation mechanism for a given molecule, respectively. With the assistance of advanced AI methods, the Uni‐Electrolyte is strongly supposed to discover new electrolyte molecules and chemical principles, promoting the practical application of next‐generation rechargeable batteries.