Theoretical Chemistry Institute Seminar: Ming Chen, Purdue University

In recent years, artificial-intelligence (AI) methodologies have been developed for a broad range of scientific applications, including molecular and materials property prediction, protein structure determination, drug discovery, and materials design. These AI-for-science approaches primarily leverage the strong expressivity of deep neural networks together with the massive volumes of experimental and computational data accumulated over decades. Despite the impressive preliminary successes of these models, major challenges remain. In particular, achieving data efficiency, ensuring physical consistency, and enabling reliable extrapolation to regimes not represented in the training data remain open questions. Incorporating physics into AI models represents a promising strategy to address these challenges. This lecture will focus on three complementary directions through which physics can be integrated into AI to enhance accuracy, interpretability, and transferability.