Machine-Learning-Guided Chemical Metathesis for In Situ Construction of High-Entropy Alloy Interphases in Li-Metal Batteries
Zenan Zhao, Zeyu Chang, Jiang Zhong, Mohan Yang, Tong Wang, Fangji Zhou, Jie Guo, Zhao Lv, Tao Song, Jing Wang, Feng Wu, Guoqiang Tan
Abstract
Designing a high-entropy alloy is a promising approach for stabilizing the Li anode, but selecting appropriate elemental compositions and developing scalable fabrication methods are key to optimizing its performance and promoting practical applications. Here, we adopt machine learning and density functional theory calculations to screen out a set of optimized alloy compositions of Fe, Co, Ni, Cu, and Zn, which demonstrate high structural strength, low strain coefficient, and strong Li adsorption. More importantly, we develop a universal and mild chemical metathesis method to construct a lithiophilic Fe–Co–Ni–Cu–Zn high-entropy alloy in situ onto Li metal. This alloy exhibits an amorphous structure with a controllable morphology and thickness. The modified Li electrode shows a homogeneous, isotropic, and dense interphase, which bears low interfacial impedance and high electrochemical stability, enabling fast charge transfer and uniform Li plating/stripping, thereby suppressing Li dendrites and side reactions. As a result, symmetric cells of the modified Li electrode achieve 900 h of stable cycling at a high current density of 10 mA cm –2, and asymmetric cells coupled with the high-loading LiFePO 4, LiNi 0.8 Co 0.1 Mn 0.1 O 2, or S/C cathodes exhibit significantly improved cell performance, especially long-term cycle stability. This study provides efficient machine learning guidance and scalable fabrication technology for developing high-entropy alloy-stabilized Li-metal anodes.