Multi-Objective Optimization of Ionic Polymer Electrolytes for High-Voltage Fast-Charging and Versatile Lithium Batteries.

Abstract

Designing ionic polymer electrolytes (IPEs) for high-voltage and fast-charging lithium batteries involves searching in a highly complex and discrete chemical space. Traditional material discovery processes struggle with this complexity due to high costs and long evaluation time. A kernel-based Bayesian optimization is described to complete the multi-objective optimization by considering ionic conductivity, electrochemical stability, and discharge capacity simultaneously. According to a recommender based on a union set of acquisition functions, promising IPEs through three iterations with only 2.8% of the chemical space is targeted. The achieved lithium metal batteries exhibit promising performance with ultrahigh cutoff voltage with NCM811 (LiNi_0.8Co_0.1Mn_0.1O₂, 4.8 V) and LNMO (LiNi_0.5Mn_1.5O₄, 4.92 V). To further extend the versatility of IPEs and diminish the high cost associated with the glove-box environment, an aqueous and high-voltage lithium-ion battery is developed by introducing water molecules in IPEs coupled with Li₄Ti₅O₁₂||LiMn₂O₄, a strong hydrogen bonding network formed between the rigid-rod polyelectrolyte and the embedded water molecules, which effectively suppresses the water reactivity, meanwhile boosting the ionic conductivity. This work reveals an innovative multi-objective optimization that effectively handles multi-targets and discontinuous parameter space, offering critical insights to address complex challenges in material discovery and property optimization for advanced and versatile lithium batteries.