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.

中文翻译：

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机器学习辅助开发用于保护锌金属阳极免受水分子腐蚀的凝胶聚合物电解质

可充电水性锌离子电池 (RAZIB) 成本低、能量密度高且安全，但面临阳极腐蚀和枝晶形成的问题。兼具高机械性能和优异电化学性能的凝胶聚合物电解质（GPE）是辅助RAZIB实际应用的有力工具。在这项工作中，在基于实验数据构建的机器学习（ML）模型的指导下，选择具有高度缠结结构的聚丙烯酰胺（PAM）来制备GPE以获得高性能RAZIB。通过控制PAM的溶胀程度，所获得的GPE有效抑制了Zn枝晶的生长，减轻了水分子对Zn金属的腐蚀，从而提高了Zn阳极的循环寿命。这些结果表明，使用基于实验数据的ML模型可以有效地帮助筛选电池材料，而高度缠结的PAM是能够平衡机械和电化学性能的优异GPE。