Battery Materials Informatics Part 2: Hard Lessons from the Field

The data is in: the application of A.I. to battery development can significantly accelerate R&D, even in cases where the underlying processes are complex and the datasets are small. But what differentiates a successful application of battery informatics from an unsuccessful one?

A lot of things, according to Aionics CEO Dr. Austin Sendek, who has overseen dozens of modeling projects in his role as Aionics CEO and as a researcher at Stanford University.

Two key factors that predict whether a battery informatics effort will be successful are the types of models used and how much is already known about the underlying dataset.

Using a combination of published and never-before-seen research to illustrate these points, Dr. Sendek will share best practices he’s observed from his work in making sure data-driven modeling is as productive as possible.

In Aionics Fortnightly Episode 8, Dr. Sendek hosted Aionics Chief Scientist Prof. Venkat Viswanathan as he detailed his learnings as a battery informatics researcher and the path that led him to Aionics — and in Episode 11, the two swap roles as Prof. Viswanathan hosts Dr. Sendek to get his perspective on the field.