Related papers: Deep learning framework for carbon nanotubes: mech…
Recent advances in the synthesis of inorganic and organic nanowires and nanotubes have provided both components for various functional devices and platforms for the study of low- dimensional transport phenomena. However, tremendous…
The nucleation of carbon nanotubes on small nickel clusters is studied using a tight binding model coupled to grand canonical Monte Carlo simulations. This technique closely follows the conditions of the synthesis of carbon nanotubes by…
The temperature dependence of mechanical properties of steel nanowires with varying carbon content was studied using molecular dynamics simulations. Four interatomic potentials were assessed, with the Modified Embedded Atom Method (MEAM)…
Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning physical…
Predicting the spatial configuration of gas molecules in nanopores of shale formations is crucial for fluid flow forecasting and hydrocarbon reserves estimation. The key challenge in these tight formations is that the majority of the pore…
Carbon nanotubes provide a unique system to study one-dimensional quantization phenomena. Scanning tunneling microscopy is used to observe the electronic wave functions that correspond to quantized energy levels in short metallic carbon…
The presence of defects strongly influences semiconductor behavior. However, predicting the electronic properties of defective materials at finite temperatures remains computationally expensive even with density functional theory due to the…
The expansiveness of compositional phase space is too vast to fully search using current theoretical tools for many emergent problems in condensed matter physics. The reliance on a deep chemical understanding is one method to identify local…
An overview is presented of the various phases predicted to occur when gases are absorbed within a bundle of carbon nanotubes. The behavior may be characterized by an effective dimensionality, which depends on the species and the…
Recently, TersoffCG, a coarse grain potential for graphene based on Tersoff potential, has been developed. In this work, we explore this potential, applying it to the case study of a single wall carbon nanotube. We performed a series of…
A complex approach phonon quantum discrete model (PQDM) was developed to describe dynamics, kinetics and statistics of phonons in carbon nanostructures with zero-chirality of both zig-zag and armchair geometry. The model allows include into…
We present a detailed description of semi-quantum molecular dynamics simulation of stochastic dynamics of a system of interacting particles. Within this approach, the dynamics of the system is described with the use of classical Newtonian…
In this article, we conduct comparative studies on the optical properties of metallic carbon nanotubes. Firstly, we compare the complex dielectric constant predicted by an analytical model, the Linear Surface Conductivity Model, with…
We calculate analytically the phase diagram of a two-dimensional square crystal and its wrapped version with defects under external homogeneous stress as a function of temperature using a simple elastic lattice model that allows for defect…
Recent experimental reports on the realizations of two-dimensional (2D) networks of the C60-based fullerenes with anisotropic and nanoporous lattices represent a significant advance, and create exciting prospects for the development of a…
We collected literature data of thermal conductivity experimentally measured in ethylene glycol-based nanofluids and investigated the influence of concentration, temperature and nanoparticle size. We implemented statistical linear…
With their unique combination of characteristics - an energy density almost 100 times that of human muscle, and a power density of 5.3 kW/kg, similar to a jet engine's output - Nylon artificial muscles stand out as particularly apt for…
We combine density-functional tight-binding (DFTB) with deep tensor neural networks (DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and vibrational molecular properties. The DTNN is used to learn a…
We use molecular dynamics simulations to study the diffusion of water inside deformed carbon nanotubes, with different degrees of eccentricity at 300K. We found a water structural transition between tubular-like to single-file for the (7,7)…
The use of CO2 as a natural refrigerant in data center cooling, oil recovery and in CO2 capture and storage which is gaining traction in recent years involves heat transfer between CO2 and the base fluid. A need arises to improve the…