Battery digital twins from the bottom up: Molecular precision at system scale

Other text in scientific journal, 2025

Compared to expensive and time-consuming experimental exploration, efficient and high-fidelity digital twins offer new pathways to accelerate the design of advanced battery technologies. In a recent study, Gong et al. presented a predictive machine learning force-field framework for liquid electrolytes in lithium-ion batteries. Achieving notable accuracy and computational efficiency, this framework addresses long-standing challenges in electrolyte simulation. By integrating molecular precision into system-level battery models, it holds potential for molecularly informed battery optimization and enhanced control strategies.