Related papers: Quantum transport in graphene nanoribbon networks:…

We implement a bottom-up multiscale approach for the modeling of defect localization in $C_{6n^2}H_{6n}$ islands, i.e. graphene quantum dots with a hexagonal symmetry, by means of density functional and semiempirical approaches. Using the…

This comprehensive study investigates charge transport through the multiple end zigzag edge states of finite-size armchair graphene nanoribbons/boron nitride nanoribbons (n-AGNR/w-BNNR) junctions under a longitudinal electric field, where n…

Electronic transport properties of monolayer graphene with extreme physical bending up to 90o angle are studied using ab Initio first-principle calculations. The importance of key structural parameters including step height, curvature…

Here we present a flexible strategy to realize robust nanomaterials exhibiting valence electronic structures whose fundamental physics is described by the SSH-Hamiltonian. These solid-state materials are realized using atomically precise…

Recently, graph neural networks (GNNs) have become an important and active research direction in deep learning. It is worth noting that most of the existing GNN-based methods learn graph representations within the Euclidean vector space.…

We simulate quantum transport between a graphene nanoribbon (GNR) and a single-walled carbon nanotube (CNT) where electrons traverse vacuum gap between them. The GNR covers CNT over a nanoscale region while their relative rotation is 90…

Factors such as the proliferation of renewable energy and electrification contribute to grid congestion as a pressing problem. Topology control is an appealing method for relieving congestion, but traditional approaches for topology…

While Graph Neural Networks (GNNs) excel on graph-structured data, their performance is fundamentally limited by the quality of the observed graph, which often contains noise, missing links, or structural properties misaligned with GNNs'…

Graph structured data, specifically text-attributed graphs (TAG), effectively represent relationships among varied entities. Such graphs are essential for semi-supervised node classification tasks. Graph Neural Networks (GNNs) have emerged…

Quantum Graph Neural Networks (QGNNs) offer a promising approach to combining quantum computing with graph-structured data processing. While classical Graph Neural Networks (GNNs) are scalable and robust, existing QGNNs often lack…

Graph neural networks (GNNs) have shown remarkable success in learning representations for graph-structured data. However, GNNs still face challenges in modeling complex phenomena that involve feature transportation. In this paper, we…

Conductance fluctuation is usually unavoidable in graphene nanoribbons (GNR) due to the presence of disorder along its edges. By measuring the low-frequency noise in GNR devices, we find that the conductance fluctuation is strongly…

We determine the characteristic of dissipative quantum transport in a coupled qubit network in the presence of on-site and off-diagonal external driving. The work is motivated by the dephasing-assisted quantum transport where noise is…

We study the transport of charge carriers through finite graphene structures. The use of numerical exact kernel polynomial and Green function techniques allows us to treat actual sized samples beyond the Dirac-cone approximation.…

An atomistic model based on the spin-restricted extended Huckel theory (EHT) is presented for simulating electronic structure and I-V characteristics of graphene devices. The model is applied to zigzag and armchair graphene nano-ribbons…

With advanced synthetic techniques, a wide variety of well-defined graphene nano-ribbons (GNRs) can be produced with atomic precision. Hence, finding the relation between their structures and properties becomes important for the rational…

We theoretically investigate the thermoelectric properties of zigzag graphene nanoribbons in the presence of extended line defects, substrate impurities and edge roughness along the nanoribbon's length. A nearest-neighbor tight-binding…

In this work, the low rank approximation concept is extended to the non-equilibrium Green's function (NEGF) method to achieve a very efficient approximated algorithm for coherent and incoherent electron transport. This new method is applied…

Graph Nerual Networks (GNNs) are effective models in graph embedding. It extracts shallow features and neighborhood information by aggregating neighbor information to learn the embedding representation of different nodes. However, the local…

Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on network structured data. Although empirically successful, GCNs exhibit certain behaviour that has no rigorous explanation -- for instance, the performance of…