Quantum transport in graphene nanoribbon networks: complexity reduction by a network decimation algorithm

TL;DR

This paper presents a new efficient decimation algorithm for quantum transport in graphene nanoribbon networks, enabling accurate modeling of conductance properties that classical methods cannot replicate.

Contribution

The authors introduce a novel network decimation algorithm that significantly reduces complexity in quantum transport calculations for GNR networks, outperforming semi-classical approaches.

Findings

The decimation algorithm accurately models quantum conductance in GNR networks.

Classical nodal analysis fails to capture quantum effects and parameter dependencies.

Quantum transport results depend strongly on network density and size.

Abstract

We study electronic quantum transport in graphene nanoribbon (GNR) networks on mesoscopic length scales. We focus on zigzag GNRs and investigate the conductance properties of statistical networks. To this end we use a density-functional-based tight-binding model to determine the electronic structure and quantum transport theory to calculate electronic transport properties. We then introduce a new efficient network decimation algorithm that reduces the complexity in generic three-diemnsional GNR networks. We compare our results to semi-classical calculations based on the nodal analysis approach and discuss the dependence of the conductance on network density and network size. We show that a nodal analysis model cannot reproduce the quantum transport results nor their dependence on model parameters well. Thus, solving the quantum network by our efficient approach is mandatory for accurate modelling the electron transport through GNR networks.