Machine-Learning-Guided Chemical Metathesis for In Situ Construction of High-Entropy Alloy Interphases in Li-Metal Batteries

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…

• NN
  National Natural Science Foundation of China

  Award: 22479013
• FR
  Fundamental Research Funds for the Central Universities

  Award: 2025CX01012