Topological interfacial states in ferroelectric domain walls of two-dimensional bismuth

TL;DR

This study uses machine learning to analyze ferroelectric domain walls in 2D bismuth, revealing topological interfacial states and energy differences that could enable novel ferroelectric devices.

Contribution

It uncovers the topological states and energy properties of domain walls in 2D bismuth, a recently discovered ferroelectric material, using machine learning techniques.

Findings

Charged domain walls have lower energy than uncharged ones.

Tail-to-tail domain walls exhibit topological interfacial states.

Energy splitting and band crossing occur due to built-in electric fields.

Abstract

Using machine learning methods, we explore different types of domain walls in the recently unveiled single-element ferroelectric, the bismuth monolayer [Nature 617, 67 (2023)]. Remarkably, our investigation reveals that the charged domain wall configuration exhibits lower energy compared to the uncharged domain wall structure. We also demonstrate that the experimentally discovered tail-to-tail domain wall maintains topological interfacial states caused by the change in the Z_2 number between ferroelectric and paraelectric states. Interestingly, due to the intrinsic built-in electric fields in asymmetry DW configurations, we find that the energy of topological interfacial states splits, resulting in an accidental band crossing at the Fermi level. Our study suggests that domain walls in two-dimensional bismuth hold potential as a promising platform for the development of ferroelectric domain wall devices.