Machine Learning Exploration of Topological Polarization Pattern in Hexagonal Boron Nitride Moiré Superlattice

Twisted moiré supercells, which can be approximated as a combination of sliding bilayers and constitute various topologically nontrivial polarization patterns, attract extensive attention recently. However, because of the excessive size of the moiré supercell, most studies are based on effective models and lack the results of first-principles calculation. In this work, machine learning to determine the topological structure of the polarization pattern is used in twisted and strained bilayers of hexagonal boron nitride (h-BN). This study further confirms that the topological pattern can be effectively modulated by the vertical electric field and lattice mismatch. Finally, local polarization also exists in the antiparallel stacked h-BN twisted and strained bilayers. This work provides a detailed study of the polarization pattern in the moiré superlattice, which believe can facilitate more research in moiré ferroelectricity, topological physics, and related fields.