Speaker
Description
Liquid electrolytes play a pivotal role in governing the performance, safety, and longevity of lithium and sodium ion batteries. However, designing optimal electrolytes remains a complex challenge due to the need to simultaneously satisfy multiple criteria, including high ionic conductivity, broad electrochemical stability, low viscosity, chemical compatibility with electrodes, and thermal stability. Complicating this further is the virtually infinite chemical design space arising from the vast combinations of solvents, salts, and additives.
Given these challenges, the integration of artificial intelligence (AI) into materials discovery is becoming not only advantageous but essential. In this talk, we present a novel AI framework trained on a large experimental dataset generated by a high-throughput robotic platform, enabling accurate prediction of the ionic conductivity of Li and Na-based liquid electrolytes. We demonstrate how this model can be used to rapidly screen and identify high-performance multi-component electrolyte formulations, including those with mixed solvents and salts, achieving state-of-the-art conductivity. This work highlights the potential of AI to significantly accelerate electrolyte discovery and guide the rational design of next-generation energy storage materials.
