Using artificial intelligence and machine learning, the system assesses electrolyte molecules across three key performance criteria, including ionic conductivity, oxidative stability, and Coulombic efficiency, properties that are often difficult to optimize simultaneously.
According to Kumar, while electrolyte development typically involves trade-offs, as molecules that offer high stability often lack conductivity, and vice versa, the new tool helps identify candidates that can meet multiple performance requirements simultaneously.