Related papers: Machine learning of microscopic ingredients for gr…
This article deals with the prediction of thermomechanical properties of fiber reinforced composites using several micromechanics models. These include strength of material approach, Halpin-Tsai equations, multi-phase mechanics of materials…
Graph neural networks (GNNs) have emerged as powerful tools for learning protein structures by capturing spatial relationships at the residue level. However, existing GNN-based methods often face challenges in learning multiscale…
We report on detailed microscopy studies of graphene and few-layer-graphene produced by mechanical exfoliation on various semi-conducting substrates. We demonstrate the possibility to prepare and analyze graphene on (001)-GaAs, manganese…
Despite the recognized importance of the multi-scale spatio-temporal organization of proteins, most computational tools can only access a limited spectrum of time and spatial scales, thereby ignoring the effects on protein behavior of the…
The ability to manufacture tailored graphene nanostructures is a key factor to fully exploit its enormous technological potential. We have investigated nanostructures created in graphene by swift heavy ion induced folding. For our…
Graphene is intrinsically non-flat and corrugates randomly. Since the corrugating physics of atomically-thin graphene is strongly tied to its electronics properties, randomly corrugating morphology of graphene poses significant challenge to…
This paper presents a novel graph-based deep learning model for tasks involving relations between two nodes (edge-centric tasks), where the focus lies on predicting relationships and interactions between pairs of nodes rather than node…
In this paper, we present a machine learning based architecture for microwave characterization of inkjet printed components on flexible substrates. Our proposed architecture uses several machine learning algorithms and automatically selects…
Automatic detection and tracking of cells in microscopy images are major applications of computer vision technologies in both biomedical research and clinical practice. Though machine learning methods are increasingly common in these…
We show how the magnetization of nano-objects can be efficiently regulated. Several types of nanosystems are considered: magnetics nanomolecules, magnetic nanoclusters, polarized nanomolecules, and magnetic graphene. These nano-objects and…
We report macroscopic sheets of highly conductive bilayer graphene with exceptionally high hole concentrations of ~ $10^{15}$ $cm^{-2}$ and unprecedented sheet resistances of 20-25 {\Omega} per square over macroscopic scales, and obtained…
In nanoscale, motion operation of a nano-objective is usually realized by displacement load, which put forwards high requirement for ductility of material. Since pristine graphene has low ductility, once the stretching strain exceeds its…
Patterning graphene into various mesoscopic devices such as nanoribbons, quantum dots, etc. by lithographic techniques has enabled the guiding and manipulation of graphene's Dirac-type charge carriers. Graphene, with well-defined strain…
In Molecular Dynamics (MD) simulations, interactions between water molecules and graphitic surfaces are often modeled as a simple Lennard-Jones potential between oxygen and carbon atoms. A possible method for tuning this parameter consists…
In computer vision tasks, features often come from diverse representations, domains (e.g., indoor and outdoor), and modalities (e.g., text, images, and videos). Effectively fusing these features is essential for robust performance,…
Carbon nanostructures are promising ballistic protection materials, due to their low density and excellent mechanical properties. Recent experimental and computational investigations on the behavior of graphene under impact conditions…
We report on a systematic study of a number of structurally identical but chemically distinct transition metal oxides in order to determine how the material-specific properties such as the composition and the strain affect the properties at…
In order to obtain a good understanding of astrochemistry, it is crucial to better understand the key parameters that govern grain-surface chemistry. For many chemical networks, these crucial parameters are the binding energies of the…
Micromechanical modeling and mechanical properties of polyurethane (PU) hybrid nanocomposite foams with multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) were investigated by mean of tensile strength, hardness, impact…
In this study, we investigate both experimentally and computationally the molecular interactions of two distinct polymers with graphene. Our experimental findings indicate that the use of a polymer mixture reduces the transfer induced…