In this talk\, I will present our recent efforts on dynami cal simulations of correlated electron systems. I will first discuss new q uantum molecular dynamics (QMD) methods based on advanced many-body techni ques\, such as Gutzwiller/slave-boson and dynamical mean-field theory\, th at are capable of modeling strong electron correlation phenomena. We apply our new QMD to simulate the correlation-induced Mott transition in a meta llic liquid\, and the nucleation-and-growth of Mott droplets in Hubbard-ty pe models. I will also discuss the implementation of the ab initio Gutzwil ler MD for simulating hydrogen liquids under high pressure. To overcome th e obstacle of huge computational complexity in such large-scale simulation s\, I will discuss how simulation efficiency can be significantly improved with the aid of modern machine learning methods. In particular\, deep-lea rning neural-network holds the potential of achieving large-scale quantum- accuracy simulation of correlated systems without the electrons.

\n DTSTART:20200918T193000Z LOCATION:Online\, Room via Zoom SUMMARY:Multiscale modeling of metal-insulator transition in correlated ele ctron systems END:VEVENT END:VCALENDAR