Math Seminar: Classical and Machine-Learning Methods for Quantum Simulation
Thomas F. Miller III, Professor of Chemistry, Division of Chemistry and Chemical Engineering, California Institute of Technology (CALTECH)
April 12, 2019
Business and Science Building (BSB), Room 334
A focus of my research is to the develop simulation methods that reveal the mechanistic details of quantum mechanical reactions that are central to biological, molecular, and heterogenous catalysis. The nature of this effort is three-fold: we combine quantum statistical mechanics and semiclassical dynamics methods to expand the scope and reliability of condensed-phase quantum dynamics simulation; we develop quantum embedding and machine learning methods that improve the description of molecular interactions and electronic properties; and we apply these methods to understand complex chemical systems. The talk will focus on recent developments  and applications  of Feynman path integral methods for the description of non-adiabatic chemical dynamics, including proton-coupled electron-transfer and long-ranged electron transfer in protein systems. Additionally, we will describe a machine-learning approach [3,4] to predicting the electronic structure results on the basis of simple molecular orbitals properties, yielding striking accuracy and transferability across chemical systems at low computational cost.
 “Path-integral isomorphic Hamiltonian for including nuclear quantum effects in non-adiabatic dynamics.” X. Tao, P. Shushkov, and T. F. Miller III, J. Chem. Phys., 148, 102327 (2018).
 “Fluctuating hydrogen-bond networks govern anomalous electron transfer kinetics in a blue copper protein.” J. S. Kretchmer, N. Boekelheide, J. J. Warren, J. R. Winkler, H. B. Gray, and T. F. Miller III, Proc. Natl. Acad. Sci. USA, 115, 6129 (2018).
 “Transferability in machine learning for electronic structure via the molecular orbital basis.” M. Welborn, L. Cheng, and T. F. Miller III, J. Chem. Theory Comput., 14, 4772 (2018).
 “A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules.” L. Cheng, M. Welborn, and T. F. Miller III, arXiv:1901.03309 (2019).
See next page.