Learning Chern Numbers of Topological Insulators with Gauge Equivariant Neural Networks

TL;DR

This paper introduces gauge equivariant neural networks to predict topological invariants (Chern numbers) in topological insulators, demonstrating their ability to generalize from trivial to non-trivial cases and establishing a new application in condensed matter physics.

Contribution

The authors develop a novel gauge equivariant neural network architecture with a specialized normalization layer for predicting Chern numbers, extending gauge-equivariant methods to condensed matter physics.

Findings

Networks trained on trivial Chern number samples generalize to non-trivial cases.

The gauge equivariant normalization layer stabilizes training.

Universal approximation theorem proven for the proposed setup.

Abstract

Equivariant network architectures are a well-established tool for predicting invariant or equivariant quantities. However, almost all learning problems considered in this context feature a global symmetry, i.e. each point of the underlying space is transformed with the same group element, as opposed to a local ``gauge'' symmetry, where each point is transformed with a different group element, exponentially enlarging the size of the symmetry group. Gauge equivariant networks have so far mainly been applied to problems in quantum chromodynamics. Here, we introduce a novel application domain for gauge-equivariant networks in the theory of topological condensed matter physics. We use gauge equivariant networks to predict topological invariants (Chern numbers) of multiband topological insulators. The gauge symmetry of the network guarantees that the predicted quantity is a topological invariant. We introduce a novel gauge equivariant normalization layer to stabilize the training and prove a universal approximation theorem for our setup. We train on samples with trivial Chern number only but show that our models generalize to samples with non-trivial Chern number. We provide various ablations of our setup. Our code is available at https://github.com/sitronsea/GENet/tree/main.