IACR AI/ML Seminar: Computational Quantum Chemistry: From Molecular Motion to Emergent Behavior and Opportunities for AI

Speaker: Zhen (Coraline) Tao (Assistant Professor of Chemistry, URI)

Date/Time/Location: Dec 10, 3pm, Avedisian Hall RM. 205.

Title: Computational Quantum Chemistry: From Molecular Motion to Emergent Behavior and Opportunities for AI

Abstract: Quantum chemistry seeks to predict molecular properties and dynamics from the fundamental interactions between electrons and nuclei. In principle, these problems are governed by the Schrödinger equation. In practice, the rapid growth of the quantum state space and the separation of electronic and nuclear degrees of freedom make direct solutions prohibitively computationally expensive. Much of computational quantum chemistry therefore deals with a central question: how can we construct tractable descriptions that retain the essential physics? In this talk, I will provide an accessible introduction to computational quantum chemistry and discuss several examples from my research. I will focus on effective descriptions of molecular motion that account for the interplay between electronic and nuclear motion, as well as a complementary question: how complex collective behavior emerges from the underlying quantum Hamiltonian. For example, I will discuss how ideas from random matrix theory can provide statistical descriptions when resolving individual quantum states becomes increasingly difficult. Finally, I will give a brief overview of how AI and machine learning are currently being explored in quantum chemistry. By introducing quantum chemistry problems alongside existing AI applications, I hope to invite discussion and collaborations to identify new questions at the interface of quantum chemistry and machine learning.