Machine learning exploration of topological polarization pattern in hexagonal boron nitride moir\'e superlattice

TL;DR

This study employs machine learning to analyze and modulate topological polarization patterns in twisted and strained hexagonal boron nitride bilayers, revealing local polarization effects and the influence of external fields.

Contribution

It introduces a machine learning approach to determine polarization topologies in large moiré superlattices, overcoming limitations of effective models and first-principles calculations.

Findings

Topological polarization patterns can be modulated by electric fields and lattice mismatch.

Local polarization exists in antiparallel stacked twisted and strained bilayers.

Machine learning effectively characterizes complex polarization structures.

Abstract

Twisted moir\'e 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 moir\'e supercell, most studies are based on effective models and lack the results of first-principles calculation. In this work, we use machine learning to determine the topological structure of the polarization pattern in twisted and strained bilayer of hexagonal boron nitride (h-BN). We further confirm 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. Our work provides a detailed study of the polarization pattern in the moir\'e superlattice, which we believe can facilitate more research in moir\'e ferroelectricity, topological physics, and related fields.