Phase diagram of disordered higher-order topological insulator: A machine learning study

TL;DR

This study uses machine learning to map the phase diagram of disordered higher-order topological insulators, demonstrating their robustness against disorder as long as the energy gap remains open.

Contribution

It applies advanced machine learning techniques to analyze the stability of higher-order topological insulators under disorder, a novel approach in this research area.

Findings

Corner states persist with disorder as long as the energy gap remains open.

Higher-order topological phases are stable against finite disorder.

The phase diagram reveals the conditions for topological phase survival.

Abstract

A higher-order topological insulator is a new concept of topological states of matter, which is characterized by the emergent boundary states whose dimensionality is lower by more than two compared with that of the bulk, and draws a considerable interest. Yet, its robustness against disorders is still unclear. Here we investigate a phase diagram of higher-order topological insulator phases in a breathing kagome model in the presence of disorders, by using a state-of-the-art machine learning technique. We find that the corner states survive against the finite strength of disorder potential as long as the energy gap is not closed, indicating the stability of the higher-order topological phases against the disorders.