We develop and apply theoretical and computational methods that span all length scales, such as quantum chemical calculations to predict reaction mechanisms, molecular simulations to study solvent-mediated processes, computational fluid dynamics to model complex flows, theories to understand principles of self-organizing systems, and numerical methods to optimize processes and supply chains. Machine learning methods are being broadly developed and applied to complement these techniques. This research area is cross-cutting and benefits from extensive collaboration with experimentalists in the department.
Machine learning, computational chemistry, molecular simulations, process modeling and optimization, computational fluid dynamics.
Core faculty: Avraamidou, Cersonsky, Graham, Klingenberg, Mavrikakis, Swaney, Van Lehn, Yin, Zavala
Affiliate faculty: Raman, Romero, Spagnolie, Venturelli
Major research centers that collaborate with or include CBE faculty: