Computational Design of Solid Electrolytes for Li-ion Batteries

Lithium (Li) metal batteries are considered as the next-generation energy-storage systems due to their high theoretical capacity and energy density. However, Li dendrite growth, interfacial instability, and the limited high-voltage stability of liquid electrolytes limit their practical application. Solid electrolytes (SEs) offer a promising alternative by improving safety and enabling high-energy-density batteries. Nevertheless, current solid-state Li metal batteries still suffer from limited cycle life and poor rate capability due to low ionic conductivity, excessive electrolyte thickness, high interfacial resistance, and uncontrolled Li dendrite growth, particularly at high current densities. This project aims to accelerate the discovery of new SEs for lithium-ion batteries by integrating large-scale materials databases with first-principles modelling. The project will leverage the existing lithium-ion SEs to analyse trends in ionic conductivity, activation energy, crystal structure, and chemical composition across reported solid electrolytes, while incorporating atomic simulation data to investigate thermodynamic stability, electronic structure, lithium migration pathways, and electrochemical stability. By combining data-driven analysis, computational screening, and machine learning, the research will establish structure–property relationships and identify key descriptors governing fast lithium-ion transport and material stability. The resulting framework will enable the rapid identification and rational design of next-generation SE materials for safer, higher-energy-density lithium-ion batteries.