Related papers: Deep learning framework for carbon nanotubes: mech…
In this paper, the concept of representation learning based on deep neural networks is applied as an alternative to the use of handcrafted features in a method for automatic visual inspection of corroded thermoelectric metallic pipes. A…
Current neural networks for predictions of molecular properties use quantum chemistry only as a source of training data. This paper explores models that use quantum chemistry as an integral part of the prediction process. This is done by…
The discovery of nanostructures and the development of growth and fabrication techniques of one- and two-dimensional materials provide the possibility to probe experimentally heat transport in low-dimensional systems. Nevertheless measuring…
Deep neural networks are increasingly being used for the analysis of medical images. However, most works neglect the uncertainty in the model's prediction. We propose an uncertainty-aware deep kernel learning model which permits the…
Understanding the per-layer learning dynamics of deep neural networks is of significant interest as it may provide insights into how neural networks learn and the potential for better training regimens. We investigate learning in Deep…
We introduce a machine-learning-based framework for constructing continuum non-Newtonian fluid dynamics model directly from a micro-scale description. Dumbbell polymer solutions are used as examples to demonstrate the essential ideas. To…
Optimized growth of Single Wall Carbon Nanotubes requires a full knowledge of the actual state of the catalyst nanoparticle and its interface with the tube. Using Tight Binding based atomistic computer simulations, we calculate carbon…
This study addresses the challenge of analyzing the growth kinetics of carbon nanotubes using in-situ homodyne polarization microscopy (HPM) by developing an automated deep learning (DL) approach. A Mask-RCNN architecture, enhanced with a…
Carbon nanotubes (CNTs) are currently considered a successor to silicon in future nanoelectronic devices. To realize this, controlled growth of defect-free nanotubes is required. Until now, the understanding of atomic-scale CNT growth…
Molecular dynamics simulations based on the adaptive intermolecular reactive empirical bond order (AIREBO) were performed to probe hydrostatic pressure induced collapse of single-walled and double-walled carbon nanotubes. It was unveiled…
We design a neural network to extract and process features from absorption images taken of one-dimensional Bose gases in the quasi-condensate regime. Specifically, the network is trained to predict both the temperature of single…
The mechanisms of deformation and failure of Cu/Nb nanolaminates manufactured by accumulated roll bonding were analysed using in-situ TEM mechanical tests. Dog-bone small-scale tensile specimens were prepared using a FIB-based technique…
Anomalous heat transport in one-dimensional nanostructures, such as nanotubes and nanowires, is a widely debated problem in condensed matter and statistical physics, with contradicting pieces of evidence from experiments and simulations.…
Deep Learning methods have seen a wide range of successful applications across different industries. Up until now, applications to physical simulations such as CFD (Computational Fluid Dynamics), have been limited to simple test-cases of…
A deep neural network was developed for the purpose of predicting thermal conductivity with a case study performed on neutron irradiated nuclear fuel. Traditional thermal conductivity modeling approaches rely on existing theoretical…
Nanoelectromechanical systems (NEMs) hold promise for a number of scientific and technological applications. In particular, NEMs oscillators have been proposed for use in ultrasensitive mass detection, radio-frequency signal processing, and…
Deep neural networks are a powerful technique that have found ample applications in several branches of Physics. In this work, we apply machine learning algorithms to a specific problem of Cosmic Ray Physics: the estimation of the muon…
We present a pipeline for predicting mechanical properties of vertically-oriented carbon nanotube (CNT) forest images using a deep learning model for artificial intelligence (AI)-based materials discovery. Our approach incorporates an…
Carbon nanotube (CNT) fibres are firmly established as a new high-performance fibre, but their tensile mechanical properties remain a relatively small fraction of those of the constituent CNTs. Clear structure-property relations and…
Multi-scale simulations of nanotube-based nanoelectromechanical systems (NEMS) controlled by a nonuniform electric field are performed by an example of a gigahertz oscillator. Using molecular dynamics simulations, we obtain the friction…