Machine-Learning-Assisted Development of Gel Polymer Electrolytes for Protecting Zn Metal Anodes from the Corrosion of Water Molecules.

Abstract

Rechargeable aqueous zinc-ion batteries (RAZIBs) offer low cost, high energy density, and safety but struggle with anode corrosion and dendrite formation. Gel polymer electrolytes (GPEs) with both high mechanical properties and excellent electrochemical properties are a powerful tool to aid the practical application of RAZIBs. In this work, guided by a machine learning (ML) model constructed based on experimental data, polyacrylamide (PAM) with a highly entangled structure was chosen to prepare GPEs for obtaining high-performance RAZIBs. By controlling the swelling degree of the PAM, the obtained GPEs effectively suppressed the growth of Zn dendrites and alleviated the corrosion of Zn metal caused by water molecules, thus improving the cycling lifespan of the Zn anode. These results indicate that using ML models based on experimental data can effectively help screen battery materials, while highly entangled PAMs are excellent GPEs capable of balancing mechanical and electrochemical properties.