Gia-Wei Chern :"Multiscale dynamical modeling of correlated electron systems"
In this talk, I will present our recent efforts on multi-scale dynamical modeling of functional electron materials, and in particular correlated electron systems. In the first part, I will discuss the utilization of machine-learning (ML) methods to achieve large-scale dynamical simulations on two canonical examples of correlated electron systems: the double-exchange and the Falicov-Kimball models. The central idea is to develop deep-learning neural-network models that can efficiently and accurately predict generalized forces required for dynamical evolutions based on local environment. The large-scale simulations enabled by the ML method also reveal new phase-ordering dynamics in these correlated electron systems. In the second part, I will discuss a new type of quantum molecular dynamics (QMD) methods based on advanced many-body techniques, such as Gutzwiller/slave-boson and dynamical mean-field theory, that are capable of modeling strong electron correlation phenomena. We apply our new QMD to simulate the correlation-induced Mott transition in a metallic liquid, and the nucleation-and-growth of Mott droplets in Hubbard-type models.
