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

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.