Electrolyte is a very important part of rechargeable batteries such as lithium batteries. However, the electrolyte innovation is facing grand challenges due to the complicated solution chemistry and infinite molecular space (>1060 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 molecule 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 module can predict the retrosynthesis pathway and reaction pathway with electrodes for a given molecule, respectively. With the assist 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.
@article{arxiv.2412.00498,
title = {Uni-Electrolyte: An Artificial Intelligence Platform for Designing Electrolyte Molecules for Rechargeable Batteries},
author = {Xiang Chen and Mingkang Liu and Shiqiu Yin and Yu-Chen Gao and Nan Yao and Qiang Zhang},
journal= {arXiv preprint arXiv:2412.00498},
year = {2024}
}