Related papers: Hybrid neural network potential for multilayer gra…

Material characterization in nano-mechanical tests requires precise interatomic potentials for the computation of atomic energies and forces with near-quantum accuracy. For such purposes, we develop a robust neural-network interatomic…

Heterogeneous graph neural networks (GNNs) have been successful in handling heterogeneous graphs. In existing heterogeneous GNNs, meta-path plays an essential role. However, recent work pointed out that simple homogeneous graph model…

Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease analysis. Recently, Graph Neural Networks (GNNs) have become…

Recently, implicit graph neural networks (GNNs) have been proposed to capture long-range dependencies in underlying graphs. In this paper, we introduce and justify two weaknesses of implicit GNNs: the constrained expressiveness due to their…

Graph Neural Network (GNN) is a variant of Deep Neural Networks (DNNs) operating on graphs. However, GNNs are more complex compared to traditional DNNs as they simultaneously exhibit features of both DNN and graph applications. As a result,…

Using full potential density functional theory calculations we have investigated the structural and electronic properties of graphene and some other graphene-like materials, viz., monolayer of SiC, GeC, BN, AlN, GaN, ZnO, ZnS and ZnSe. We…

Here we report on technology developments implemented into the Graphene Flagship European project for the integration of graphene and graphene-related materials (GRMs) into energy application devices. Many of the technologies investigated…

Graph-based representations for samples of computational mechanics-related datasets can prove instrumental when dealing with problems like irregular domains or molecular structures of materials, etc. To effectively analyze and process such…

We investigate the electronic structure of graphene monolayers subjected to patterned dielectric superlattices. Through a quantum capacitance model approach, we simulate realistic devices capable of imposing periodic potentials on graphene.…

Germanium manganese compounds exhibit a variety of stable and metastable phases with different stoichiometry. These materials entail interesting electronic, magnetic and thermal properties both in their bulk form and as heterostructures.…

Graph neural networks (GNNs) have become powerful tools for processing graph-based information in various domains. A desirable property of GNNs is transferability, where a trained network can swap in information from a different graph…

Combining both vertical and in-plane two-dimensional (2D) heterostructures opens up the possibility to create an unprecedented architecture using 2D atomic layer building blocks. The thermal transport properties of such mixed…

Rhombohedral graphene (rG) aligned with hexagonal boron nitride (hBN) has been shown to host flat bands that stabilize various strongly correlated quantum phases, including Mott insulators, integer, and fractional quantum anomalous Hall…

In recent years, machine learning interatomic potentials (MLIPs) have attracted significant attention as a method that enables large-scale, long-time atomistic simulations while maintaining accuracy comparable to electronic structure…

We investigate through analytic calculations the surface plasmon dispersion relation for monolayer graphene sheets and a separated parallel pair of graphene monolayers. An approximate form for the dispersion relation for the monolayer case…

Thermal properties of graphene monolayers are studied by path-integral molecular dynamics (PIMD) simulations, which take into account the quantization of vibrational modes in the crystalline membrane, and allow one to consider anharmonic…

Large area graphene sheets grown by chemical vapor deposition can potentially be employed as a transparent electrode in photovoltaics if their sheet resistance can be significantly lowered, without any loss in transparency. Here, we report…

In this work, we have studied the phonon properties of multi-layered graphene with the use of Molecular Dynamics (MD) simulations and the k-space Autocorrelation Sequence (k-VACS) method. We calculate the phonon dispersion curves, densities…

Inelastic electron tunneling provides a low-energy pathway for the excitation of surface plasmons and light emission. We theoretically investigate tunnel junctions based on metals and graphene. We show that graphene is potentially a highly…

Graphene plasmonics has become a highlighted research area due to the outstanding properties of deep-subwavelength plasmon excitation, long relaxation time, and electro-optical tunability. Although the giant conductivity of a graphene layer…