Building a Better Battery with Machine Learning

Discovering promising new materials is central to our ability to design better batteries, but research over the last several decades has been driven by inefficient guess-and-check searches that have resulted in slow progress. Focusing on solid-state electrolyte materials, Austin Sendek built a data-driven model for predicting material performance by applying machine learning to a small set of 40 experimental data points on crystal structure and ionic conductivity from the literature. He used the resulting model to guide an experimental search for high ionic conductivity electrolyte materials and found that incorporating machine learning into the search leads to several times more discoveries than a comparable guess-and-check effort.