BEGIN:VCALENDAR VERSION:2.0 PRODID:Data::ICal 0.22 BEGIN:VEVENT DESCRIPTION:Sheng Zhang\, University of Virginia - Department of Physics\n\ n
The Falicov-Kimball (FK) model was initially introduced as a statistic al model for metal-insulator transition in correlated electron systems. It can be exactly solved by combining the classical Monte Carlo method for t he lattice gas and exact diagonalization (ED) for the itinerant electrons. However\, direct ED calculation\, which is required in each time-step of dynamical simulations of the FK model\, is very time-consuming. Here we ap ply the modern machine learning (ML) technique to enable the first-ever la rge-scale kinetic Monte Carlo (kMC) simulations of FK model. Using our neu ral-network model on a system of unprecedented 105 lattice site s\, we uncover an intriguing hidden sub-lattice symmetry breaking in the p hase separation dynamics of FK model.
\n DTSTART:20210415T193000Z LOCATION:Online\, Room via Zoom SUMMARY:Machine Learning Enable the Large Scale Kinetic Monte Carlo for Fal icov-Kimball Model END:VEVENT END:VCALENDAR