Next-gen EV batteries use solid electrolytes to double lithium cell range, life
The University of Chicago research team manually compiled battery data from 250 studies to train their AI model.
A research team from the U.S. has developed an artificial intelligence-based framework that can potentially speed up the development of next-generation batteries by identifying molecules with ideal electrolyte properties.
The new method, developed by Ritesh Kumar, PhD, a postdoctoral fellow working in the Amanchukwu Lab at the University of Chicago Pritzker School of Molecular Engineering (UChicago PME) and his team, evaluates and ranks potential battery electrolyte candidates using a metric called the ‘eScore’.
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.