Related papers: Dissertation Machine Learning in Materials Science…
Machine learning has been applied to the problem of X-ray diffraction phase prediction with promising results. In this paper, we describe a method for using machine learning to predict crystal structure phases from X-ray diffraction data of…
We have characterized the conductivity of carbon nanotubes (CNT) fibers enriched in semiconducting species as a function of temperature and pulsed laser irradiation of 266 nm wavelength. While at high temperatures the response approaches an…
Time-dependent fields are a valuable tool to control fundamental quantum phenomena in highly coherent low dimensional electron systems. Carbon nanotubes and graphene are a promising ground for these studies. Here we offer a brief overview…
The paper aims at the development of a digital twin modelling the electrical conductivity of multi-wall carbon nanotube (MWCNT)/polymer nanocomposite. A comparative analysis is conducted for various formulations of boundary conditions in…
Over the past decade, deep learning models have exhibited considerable advancements, reaching or even exceeding human-level performance in a range of visual perception tasks. This remarkable progress has sparked interest in applying deep…
Advanced electronic device technologies require a faster operation and smaller average power consumption, which are the most important parameters in very large scale integrated circuit design. The conventional Complementary Metal-Oxide…
Quantum neural network (QNN) models have received increasing attention owing to their strong expressibility and resistance to overfitting. It is particularly useful when the size of the training data is small, making it a good fit for…
Printed electronics rely on the deposition of conductive liquid inks, typically onto polymeric or paper substrates. Among available conductive fillers for use in electronic inks, carbon nanotubes (CNTs) have high conductivity, low density,…
The present research aims to provide a practical numerical tool for the mechanical analysis of nanoscale trusses with similar accuracy to molecular dynamics (MD). As a first step, MD simulations of uniaxial tensile and compression tests of…
There has been a recent surge of interest in using machine learning to approximate density functional theory (DFT) in materials science. However, many of the most performant models are evaluated on large databases of computed properties of,…
We compute electrostatic fields induced deformations of cantilevered finite-length metallic carbon nanotubes, using an energy minimization method based on a charge-dipole moment interaction potential combined with an empirical many-body…
Carbon nanotubes are a fraction of the size of transistors used in today's best microchips, as it could reduce power demands and heating in next electronics revolution. Present study investigates the electronic and magnetic structure of…
Recent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good experimental parameters, i.e. tuning up devices. In many…
We give a brief account of the current limitations and possibilities in the field of computational simulation of materials. We then focus on the effect that the emergence of machine learning interatomic potentials is having on the field and…
Classification and identification of different phases and the transitions between them is a central task in condensed matter physics. Machine learning, which has achieved dramatic success in a wide range of applications, holds the promise…
Nowadays, Neural Networks represent a major expectation for the realization of powerful Deep Learning algorithms, which can determine several physical systems' behaviors and operations. Computational resources required for model, training,…
This paper reviews machine learning applications and approaches to detection, classification and control of intelligent materials and structures with embedded distributed computation elements. The purpose of this survey is to identify…
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…
Careful research on different materials reveals that the material properties are mostly affected by the size of it. Material size down to nanometer scale exhibits some remarkable properties, resulting in unique physical and chemical…
Attracted by the importance of new materials in nanotechnology area, this thesis develops this research field while deepening results. We started by introducing the more sophisticated simulation and calculation methods, such as the Monte…