Abstract:

　　A focus of my research is to thedevelop simulation methods that reveal the mechanistic details of quantummechanical reactions that are central to biological, molecular, andheterogenous catalysis. The nature of this effort is three-fold: we work from thefoundation of quantum statistical mechanics and semiclassical dynamics todevelop methods that significantly expand the scope and reliability ofcondensed-phase quantum dynamics simulation; we develop quantum embedding andmachine learning methods that improve the description of molecular interactionsand electronic properties; and we apply these methods to understand complexchemical systems.

　　The talk will focus on recentdevelopments and applications of Feynman path integral methods for thedescription of non-adiabatic chemical dynamics, including proton-coupledelectron-transfer and long-ranged electron transfer in protein systems. Additionally,we will describe a machine-learning approach to predicting the electronicstructure results on the basis of simple molecular orbitals properties,yielding striking accuracy and transferability across chemical systems at lowcomputational cost.

About the speaker:

　　Thomas Miller’sresearch focuses on the development of theoretical and computational methods tostudy chemical processes that are related to catalysis, battery technologies,and membrane protein biosynthesis. After completing his undergraduate studiesat Texas A&M University, he attended graduate school in the UK on a BritishMarshall Scholarship and received his Ph.D. from Oxford University in 2005.Miller then returned to the US for a postdoctoral fellowship at UC Berkeley. Hejoined the faculty of the California Institute of Technology in 2008 and waspromoted to full professor in 2013. While at Caltech, he has received awardsthat include the Sloan Research Fellowship, NSF CAREER Award, AssociatedStudents of Caltech Teaching Award, Dreyfus Teacher-Scholar Award, and the ACSEarly-Career Award in Theoretical Chemistry.