Austin Sendek, PhD, Founder/CEO, Aionics, Inc.; Visiting Scholar, Stanford University
Discovering promising new materials is central to our ability to design better batteries, but research progress can be limited by an incomplete understanding of structure/property relationships, slow testing cycles, and overwhelmingly large numbers of candidate materials. New machine learning (ML) approaches offer a route to accelerated materials discovery by training predictive models on existing experimental data and then using these models to screen databases of candidate materials. Our research at Stanford University suggests that careful ML modeling can provide a significant acceleration in the rate of new materials discovery, even when trained on small amounts of data. In this talk, we present our research in using ML to accelerate electrode and electrolyte discovery, discuss best practices for the application of ML to materials design, and highlight the Aionics materials design software platform.