Scientists turn to AI to design better battery electrolytes
Designing a battery is a three-part process. You need a positive electrode, you need a negative electrode, and—importantly—you need an electrolyte that works with both electrodes.
An agentic AI tool for battery researchers harnesses data from previous battery designs to predict the cycle life of new battery concepts. With information from just 50 cycles, the tool—developed at University of Michigan Engineering—can predict how many charge-discharge cycles the battery can undergo before its capacity drops below 90%…
Battery electrolytes aren't just one chemical, but a complex mixture of salts, solvents, and additives interacting and reacting with each other. Artificial intelligence has made great headway in helping select ideal materials to go into that chemical soup. But a team from the University of Chicago Pritzker School of Molecular…
An international collaborative research team has developed an image recognition technology that can accurately determine the elemental composition and the number of charge and discharge cycles of a battery by examining only its surface morphology using AI learning.