Related papers: Deep learning framework for carbon nanotubes: mech…
New plans should be used to improve quality and to increase productivity and durability of conventional pavements. In this investigation, it has been attempted to promote technical characteristics of bitumen using carbon nanotubes as an…
The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the…
A study based on ab initio calculations is presented on the estructural, elastic, and vibrational properties of single-wall carbon nanotubes with different radii and chiralities. We use SIESTA, an implementation of…
A new mesoscale mechanical model, describing elastic interactions in carbon nanotubes (CNT) and other nanofilaments, is proposed. Functional form of the developed model is based on enhanced vector model (EVM) that describes basic types of…
Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the $ab$ $initio$ framework due to its computational intensity. Here we show DeePTB, an efficient deep learning-based…
The deep neural network has attained significant efficiency in image recognition. However, it has vulnerable recognition robustness under extensive data uncertainty in practical applications. The uncertainty is attributed to the inevitable…
We report results of theoretical studies on the elastic properties of single-wall nanotubes of the following compositions: C, BN, ${BC}_3$, ${BC}_2{N}$ and ${C}_3{N}_4$. These studies have been carried out using a total energy,…
Carbon nanotube transistors have been experimentally demonstrated to reach performance comparable and even surpassing that of silicon transistors. Further improvement requires addressing non-idealities arising from device fabrication that…
Nanotruss structures made of carbon nanotubes are investigated in two conceptual applications: either as building blocks of metamaterials or for nanostructural applications. The nanotrusses are optimized for different purposes, including…
Machine Learning tools are nowadays widely applied extensively to the prediction of the properties of molecular materials, using datasets extracted from high-throughput computational models. In several cases of scientific and technological…
The photoacoustic method with piezoelectric detection for the simultaneous evaluation of the thermophysical properties is proposed. The approach is based on the settling of an additional heat sink for redistribution of heat fluxes deposited…
Carbon nanotubes are modeled as point configurations and investigated by minimizing configurational energies including two-and three-body interactions. Optimal configurations are identified with local minima and their fine geometry is fully…
Ultra-precision machining of metals, the breaking of nanowires under tensile stress and fracture of nanoscale materials are examples of technologically important processes which are both extremely difficult and costly to investigate…
Based on deep neural networks (DNNs), deep learning has been successfully applied to many problems, but its mechanism is still not well understood -- especially the reason why over-parametrized DNNs can generalize. A recent statistical…
The structures and symmetries of single-walled carbon nanotubes (SWNTs) are introduced in detail. The physical properties of SWNTs induced by their symmetries can be described by tensors in mathematical point of view. It is found that there…
Bulk samples of carbon multilayer nanotubes with the structure of nested cones (fishbone structure) suitable for transport measurements, were prepared by compressing under high pressure (~25 kbar) a nanotube precursor synthesized through…
Carbon nanotube (CNT) fiber is formed by assembling millions of individual tubes. The assembly feature provides the fiber with rich interface structures and thus various ways of energy dissipation, as reflected by the non-zero loss tangent…
The temperature distributions in current-carrying carbon nanotubes have been measured with a scanning thermal microscope. The obtained temperature profiles reveal diffusive and dissipative electron transport in multi-walled nanotubes and in…
A machine-learning non-contact method to determine the temperature of a laser gain medium via its laser emission with a trained few-layer neural net model is presented. The training of the feed-forward Neural Network (NN) enables the…
Manufacturing of microstructures using a microfluidic device is a largely empirical effort due to the multi-physical nature of the fabrication process. As such, models are desired that will predict microstructure performance characteristics…