Machine Learning‐Assisted KCl‐CaCl ₂ ‐LiCl Electrolyte Design for Low‐Temperature, High‐Performance Calcium‐Based Liquid Metal Batteries

ABSTRACT Calcium‐based liquid metal batteries are promising for large‐scale energy storage due to calcium abundance and low cost, yet their practical applications are impeded by high operating temperatures, severe self‐discharge, limited coulombic efficiency, and rapid capacity fading. Here, we develop a machine learning (ML)‐assisted optimization framework, integrating data‐driven analysis, ML prediction, and experimental verification to design a high‐performance ternary molten‐salt electrolyte. Through multi‐parameter evaluation of thermodynamic stability, melting behavior, density, and cost, KCl was identified as an optimal third component for CaCl ₂ ‐LiCl based systems. A multidimensional descriptor‐performance dataset was constructed to develop a random forest model for precise electrolyte composition optimization. Guided by this model, the KCl‐CaCl ₂ ‐LiCl electrolyte (13:35:52 mol%) was experimentally verified to enable stable operation at 480°C, delivering a coulombic efficiency >99.5%, an ultralow self‐discharge current density of 0.31 mA cm ⁻² , >91% capacity retention after 100 cycles, while maintaining a low material cost of 0.81$ kg ⁻¹ . This optimized ternary electrolyte suppresses calcium dissolution through cooperative multi‐cation effects, significantly improving low‐temperature electrochemical performance and cycling stability. This work not only provides a viable pathway toward practical Ca‐based LMBs but also establishes a generalizable ML‐assisted paradigm for accelerated electrolyte design in advanced electrochemical energy storage.