Speaker
Description
Moiré superlattices, formed by stacking layered 2D materials with a twist in orientation, have emerged as a new platform for exploration of new physics and exotic quantum phenomena. The twist-angle dependent moiré effects and superlattice potentials offer a new route in materials design and quantum engineering. However, a direct prediction of the superlattice potential remains challenging due to the large moiré periodicity and associated computational demand. In this talk, I will introduce our computational approaches combining Density Functional Theory (DFT) calculations, machine learning and classical physics models to understand and predict the superlattice potentials for twisted bilayer hexagonal boron nitride. Optimizing the atomic structure using machine-learned interatomic potential reveals significant structural reconstructions. We introduce an efficient classical physics model that enables predictive and interpretative insights into the formation of superlattice potentials in these twisted systems.
