Energy Storage Breakthroughs Enable a Strong & Secure Energy Landscape at Argonne - CleanTechnica

Researchers at the University of Michigan, leveraging the supercomputing resources at the U.S. Department of Energy’s Argonne National Laboratory, are pioneering the use of artificial intelligence (AI) foundation models to accelerate the discovery of advanced battery materials. Traditionally, battery material development relied heavily on intuition and incremental improvements to a limited set of materials discovered mainly between 1975 and 1985. The new AI-driven approach uses large, specialized models trained on massive datasets of molecular information to predict key properties such as conductivity, melting point, and flammability, enabling more targeted exploration of potential battery electrolytes and electrodes. The scale of possible molecular compounds—estimated at around 10^60—makes traditional trial-and-error methods impractical. The AI foundation models, trained on billions of known molecules, can efficiently navigate this vast chemical space by identifying promising candidates with desirable properties for next-generation batteries. In 2024, the team utilized Argonne’s Polaris supercomputer to train one of the largest chemical foundation models