Abstract:
In the Accelerated Materials Design and Discovery (AMDD) program at TRI, we apply AI and data-driven methods to the challenge of materials design. In this talk, we highlight two projects that avoid the atomic-scale representation commonly chosen for inorganic solid state materials: a model for predicting oxidation states (https://oxi.matr.io/), and multimodal models for materials. We discuss how these projects address our priorities of providing actionable insights for materials experiment, as well as gaining understanding to develop materials theory.

Bio:
Linda Hung is a Senior Manager in the Energy & Materials Division at Toyota Research Institute, and an associate editor for the journal Digital Discovery. She obtained her PhD in applied and computational mathematics from Princeton University and has held research positions at the Ecole Polytechnique (France), the University of Illinois Chicago, and NIST before joining TRI in 2017.