Layer-Resolved Quantum Transport in Twisted Bilayer Graphene: Counterflow and Machine Learning Predictions

TL;DR

This study explores layer-specific quantum transport in twisted bilayer graphene, revealing counterflow currents and employing machine learning to predict conductance, with implications for understanding interlayer interactions and device responses.

Contribution

It provides the first detailed analysis of layer-resolved transport phenomena in twisted bilayer graphene and introduces a machine learning model to predict conductance based on complex parameters.

Findings

Counterflow currents in top layers observed in different device geometries.

Counterflow persists despite disorder, weak contact coupling, and lattice relaxation.

Machine learning accurately predicts conductance variations with twist angle and energy.

Abstract

The layer-resolved quantum transport response of a twisted bilayer graphene device is investigated by driving a current through the bottom layer and measuring the induced voltage in the top layer. Devices with four- and eight-layer differentiated contacts were analyzed, revealing that in a nanoribbon geometry (four contacts), a longitudinal counterflow current emerges in the top layer, while in a square-junction configuration (eight contacts), this counterflow is accompanied by a transverse, or Hall, component. These effects persist despite weak coupling to contacts, onsite disorder, lattice relaxation and variations in device size. The observed counterflow response indicates a circulating interlayer current, which generates an in-plane magnetic moment excited by the injected current. Finally, due to the intricate relationship between the electrical layer response, energy, and twist angle, a clusterized machine learning model was trained, validated, and tested to predict various conductances.