DMI/MEMS Seminar Presented by Asst. Prof. Thomas A.R. Purcell, Ph.D.

Title: Automating Computational Materials Design with Explainable AI

Abstract: High-throughput density functional theory (DFT) calculations have the capability to quickly screen thousands of materials to identify the top candidates for myriad of energy and sustainability applications. However, despite its impressive efficiency relative to experiments, computational screening is still incapable of exploring the entirety of materials space, which is needed to find the optimal candidate structures. Incorporating artificial intelligence (AI) models into these frameworks would further accelerate these searches by focusing on the most promising candidates as early as possible, but are often limited by a scarcity of of available data. Here I will present the Purcell Lab's recent efforts to develop new high-throughput workflows to describe various materials properties for solid-state and polymer materials. In particular I will focus on how incorporating explainable AI into these frameworks allows them to not only focus the searches on the optimal regions of materials space, but also extract design rules to guide future experimental studies. Finally, I will showcase how these workflow architectures can be adapted for verifying the consistency of DFT calculations across different implementations.