Machine learning-assisted benign transformation of three zinc states in zinc ion batteries

Abstract

The applications of aqueous zinc-ion batteries (ZIBs) are limited by challenges such as zinc dendrite formation and hydrogen evolution. However, the traditional trial-and-error approach to designing interface layer structures has proven to be both inefficient and costly. To address this issue, a fully connected neural network model has been developed to analyze the relationship between the structure and stability across 168 000 interface layer candidates. Leveraging the power of machine learning, a cerium-iron bimetallic metal–organic framework interface layer has been successfully designed. This layer contains ion-sieving channels, polar –CN organic ligands, and a zincophilic Ce cation, which play crucial roles in reducing the desolvation energy barrier for the conversion of Zn(H₂O)_n²⁺ to Zn²⁺. Coordination channels ensure selective ion transport and facilitate uniform Zn²⁺–Zn transformation, which prevents zinc dendrite formation. As a result, this continuous transformation among Zn(H₂O)_n²⁺, Zn²⁺, and Zn states leads to long-term stability. The MOF@Zn‖MOF@Zn configuration attains an impressive cycle life of over 4300 hours at 1 mA cm⁻² (1 mA h cm⁻²). Furthermore, the MOF@Zn‖Cu configuration exhibits an average coulombic efficiency of 99.8% over 1400 cycles at 2 mA cm⁻² (1 mA h cm⁻²). This study proposes a straightforward and effective protective layer strategy for significantly improving the stability of the zinc anode.