Apr 8, 2025
2:30pm - 3:00pm
Summit, Level 4, Room 441
Ting Cao1
University of Washington1
This talk will show our recent theoretical and computational investigations into moiré superlattices. We start by demonstrating that a deep neural network guided by first-principles data can be used to examine moiré structural reconstruction in various homobilayers and heterobilayers of transition metal dichalcogenides. Going beyond the capacity of direct DFT calculations, our machine-learning enabled workflow discovers salient structural features and key topological characters controlled by twist angles, layer composition, and other tuning knobs. This knowledge can be used to predict new forms of moiré potential and moiré topology, which enable the study of novel excited states. We connect our theoretical discoveries to experimental results and explore potential applications.