Related papers: Quantum transport in graphene nanoribbon networks:…
Atomically precise graphene nanoribbons are a promising emerging class of designer quantum materials with electronic properties that are tunable by chemical design. However, many challenges remain in the device integration of these…
We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation. To capture structural information associated with graphs, we investigate the problem of encoding graphs using…
The atomically-precise controlled synthesis of graphene stripes embedded in hexagonal boron nitride opens up new possibilities for the construction of nanodevices with applications in sensing. Here, we explore properties related to…
Potential and mass barriers in graphene introduce electron scattering, modulating transmission probabilities. Complex multi-barrier setups allow electron transmission to be controlled with high precision, but have a huge design space of…
Two-dimensional graphene, carbon nanotubes and graphene nanoribbons represent a novel class of low dimensional materials that could serve as building blocks for future carbon-based nanoelectronics. Although these systems share a similar…
We have studied quantum transport in Graphene Nanoribbon Tunnel Field-Effect Transistors. Unlike other studies on similar structures, we have included dissipative processes induced by inelastic electron-phonon scattering and edge roughness…
The vehicle routing problem (VRP) is a fundamental NP-hard task in intelligent transportation systems with broad applications in logistics and distribution. Deep reinforcement learning (DRL) with Graph Neural Networks (GNNs) has shown…
Graphene nanoribbon (GNR) emerges as an exceptionally promising channel candidate due to its tunable sizable bandgap (0-3 eV), ultrahigh carrier mobility (up to 4600 cm^(2) V^(-1) s^(-1)), and excellent device performance (current on-off…
Carbon nanotube networks are one of the candidate materials to function as malleable, transparent, conducting films, with the technologically promising application of being used as flexible electronic displays. Nanotubes disorderly…
Quantum machine learning is a fast-emerging field that aims to tackle machine learning using quantum algorithms and quantum computing. Due to the lack of physical qubits and an effective means to map real-world data from Euclidean space to…
We present an ab initio approach to electronic transport in nanoscale systems which includes electronic correlations through the GW approximation. With respect to Landauer approaches based on density-functional theory (DFT), we introduce a…
Graphene nanoribbons (GNRs) have attracted a strong interest from researchers worldwide, as they constitute an emerging class of quantum-designed materials. The major challenges towards their exploitation in electronic applications include…
Graphene nanoribbon (GNR) field-effect transistors (FETs) with widths down to 12 nm have been fabricated by electron beam lithography using a wafer-scale chemical vapor deposition (CVD) process to form the graphene. The GNR FETs show…
On-surface synthesis enables the fabrication of atomically precise graphene nanoribbons (GNRs) with properties defined by their shape and edge topology. While this bottom-up approach provides unmatched control over electronic and structural…
During the last 15 years bottom-up on-surface synthesis has been demonstrated as an efficient way to synthesize carbon nanostructures with atomic precision, opening the door to unprecedented electronic control at the nanoscale. Nanoporous…
Motivated by recent experiments of successfully carving out stable carbon atomic chains from graphene, we investigate a device structure of a carbon chain connecting two zigzag graphene nanoribbons with highly tunable spin-dependent…
By combining density functional theory and nonequilibrium Green's function, we study the electronic and transport properties of monolayer black phosphorus nanoribbons (PNRs). First, we investigate the band-gap of PNRs and its modulation by…
We present a theory to study gas molecules adsorption on armchair graphene nanoribbons (AGNRs) by applying the results of \emph{ab} \emph{initio} calculations to the single-band tight-binding approximation. In addition, the effect of edge…
We derive a fluid-dynamic model for electron transport near a Dirac point in graphene. The derivation is based on the minimum entropy principle, which is exploited in order to close fluid-dynamic equations for quantum mixed states. To this…
We study electronic transport through a strongly interacting quantum dot by using the finite temperature extension of Wilson's numerical renormalization group (NRG) method. This allows the linear conductance to be calculated at all…