Related papers: Deep learning framework for carbon nanotubes: mech…
We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular…
Prediction of toxicity levels of chemical compounds is an important issue in Quantitative Structure-Activity Relationship (QSAR) modeling. Although toxicity prediction has achieved significant progress in recent times through deep learning,…
The dependence of the energy of interwall interaction in double-walled carbon nanotubes (DWNT) on the relative position of walls has been calculated using the density functional method. This dependence is used to evaluate forces that are…
Order(N) Tight-Binding Molecular Dynamics (TBMD) simulations are performed to investigate the thermal stability of (10,10) metallic Single-Walled Carbon Nanotubes (SWCNT). Periodic boundary conditions (PBC) are applied in axial direction.…
Carbon nanotubes (CNTs) are principal constituents of nanocomposites and nano-systems. CNT size-dependent response assessment is therefore a topic of current interest in Mechanics of Advanced Materials and Structures. CNTs are modeled here…
A direct and local deep learning (DL) model for atomic forces is presented. We demonstrate the model performance in bulk aluminum, sodium, and silicon; and show that its errors are comparable to those found in state-of-the-art machine…
We use molecular dynamics simulations to study the diffusion of water inside deformed carbon nanotubes with different degrees of deformation at 300 K. We found that the number of hydrogen bonds that water forms depends on nanotube topology,…
We present a machine learning based model that can predict the electronic structure of quasi-one-dimensional materials while they are subjected to deformation modes such as torsion and extension/compression. The technique described here…
Recent air pollution issues have raised significant attention to develop efficient air filters, and one of the most promising candidates is that enabled by nanofibers. We explore here selective molecular capture mechanism for volatile…
Effective thermal conductivity is an important property of composites for different thermal management applications. Although physics-based methods, such as effective medium theory and solving partial differential equation, dominate the…
The transport properties of a CNT capacitively coupled to a molecule vibrating along one of its librational modes are studied and its transport properties analyzed in the presence of an STM tip. We evaluate the linear charge and thermal…
We present a physics-constrained control-oriented deep learning method for modeling building thermal dynamics. The proposed method is based on the systematic encoding of physics-based prior knowledge into a structured recurrent neural…
Nanofluids are suspensions of nanoparticles and fibers which have recently attracted much attention due to their superior thermal properties. Here, nanofluids are studied in the sense of nanofins transversally attached to a surface, so that…
We report a study of the rotational dynamics in double-walled nanotubes using molecular dynamics simulations and a simple analytical model reproducing very well the observations. We show that the dynamic friction is linear in the angular…
As the need for miniaturized structural and functional materials has increased,the need for precise materials characterizaton has also expanded. Nanoindentation is a popular method that can be used to measure material mechanical behavior…
Active learning methods are rapidly becoming the integral component of automated experiment workflows in imaging, materials synthesis, and computation. The distinctive aspect of many experimental scenarios is the presence of multiple…
The ultrathin one-dimensional sp3 diamond nanothreads (NTHs), as successfully synthesised recently, have greatly augmented the interests from the carbon community. In principle, there can exist different stable NTH structures. In this work,…
Transistors are the basic building blocks for all electronics. Accurate prediction of their current-voltage (IV) characteristics enables circuit simulations before the expensive silicon tape-out. In this work, we propose using deep neural…
Understanding the kinetic selectivity of carbon nanotube growth at the scale of individual nanotubes is essential for the development of high chiral selectivity growth methods. Here we demonstrate that homodyne polarization microscopy can…
Possible applications of carbon nanotubes in nanoelectromechanical systems (NEMSs) based on rela-tive motion and interaction of nanotubes walls are considered for wide set of NEMS. This set includes nanomotors, nanoactuator, nanorelay,…