MSE Seminar – Prof. Chris CHIPOT (27 January 2026)

Understanding rare events in complex molecular systems requires identifying transition pathways that are both physically meaningful and committor-consistent. Prof. Chipot will introduce a neural-network-based framework that simultaneously learns the committor function and the associated committor-consistent transition pathways, providing a unified, data-driven perspective on molecular transformations. The method, rooted in the committor time-correlation function, operates beyond the overdamped diffusive limit and distinguishes multiple competing mechanisms. Next, leveraging geometric graph neural networks built on vector perceptrons, Prof. Chipot will show how the committor can be learned directly from atomic coordinates—bypassing the need for predefined collective variables—and yields atom-level interpretability by pinpointing the key atomic contributors to the transition. Last, Prof. Chipot will present an iterative refinement between biased sampling and committor learning to identify dominant transition channels, enhance sampling efficiency, and enable accurate estimation of rate constants. Demonstrations on benchmark potentials and molecular systems, from peptide isomerization and folding models to chemical reactions, showcase the robustness, transferability, and ability to uncover physically grounded, of these committor-consistent descriptions of complex molecular processes.