Polarization-driven band topology evolution in twisted MoTe$_2$ and WSe$_2$

TL;DR

This study uses large-scale DFT calculations with machine learning to explore how moiré band topology in twisted MoTe$_2$ and WSe$_2$ changes with twist angle, revealing a sign reversal driven by competing ferroelectric and piezoelectric effects.

Contribution

It introduces a machine learning-accelerated large-scale DFT approach to analyze moiré band topology evolution in twisted bilayer TMDs, highlighting the role of electrostatic effects.

Findings

Chern numbers change sign with twist angle

Ferroelectricity and piezoelectricity compete to influence topology

Potential to mimic Landau-level physics without magnetic fields

Abstract

Motivated by recent experimental observations of opposite Chern numbers in $R$-type twisted MoTe$_2$ and WSe$_2$ homobilayers, we perform large-scale density-functional-theory (DFT) calculations with machine learning force fields to investigate moir\'e band topology from large to small twist angles in both materials. We find that the Chern numbers of the moir\'e frontier bands change sign as a function of twist angle, and this change is driven by the competition between moir\'{e} ferroelectricity and piezoelectricity. Our large-scale calculations, enabled by machine learning methods, reveal crucial insights into interactions across different scales in twisted bilayer systems. The interplay between atomic-level relaxation effects and moir\'e-scale electrostatic potential variation opens new avenues for the design of intertwined topological and correlated states, including the possibility of mimicking higher Landau-level physics in the absence of a magnetic field.