Machine learning guided discovery of stable, spin-resolved topological insulators

TL;DR

This paper develops a neural network to identify solid-state insulators with an odd, quantized spin-Chern number despite a trivial $ ext{Z}_2$ index, discovering Ti$_2$CO$_2$ as a promising candidate for Majorana modes.

Contribution

A neural network is introduced to efficiently identify systems with odd spin-Chern numbers and trivial $ ext{Z}_2$ index, enabling discovery of new topological insulators.

Findings

Neural network successfully identifies Ti$_2$CO$_2$ as a candidate.

Ti$_2$CO$_2$ can support Majorana corner modes.

First solid-state system with trivial $ ext{Z}_2$ and odd $C_s$ found.

Abstract

Identification of a non-trivial $\mathbb{Z}_{2}$ index in a spinful two dimensional insulator indicates the presence of an odd, quantized (pseudo)spin-resolved Chern number, $C_{s}=(C_{\uparrow}-C_{\downarrow})/2$. However, the statement is not biconditional. An odd spin-Chern number can survive when the familiar $\mathbb{Z}_{2}$ index vanishes. Identification of solid-state systems hosting an odd, quantized $C_{s}$ and trivial $\mathbb{Z}_{2}$ index is a pressing issue due to the potential for such insulators to admit band gaps optimal for experiments and quantum devices. Nevertheless, they have proven elusive due to the computational expense associated with their discovery. In this work, a neural network capable of identifying the spin-Chern number is developed and used to identify the first solid-state systems hosting a trivial $\mathbb{Z}_{2}$ index and odd $C_{s}$. We demonstrate the potential of one such system, Ti$_{2}$CO$_{2}$, to support Majorana corner modes via the superconducting proximity effect.