Related papers: Dissertation Machine Learning in Materials Science…
This study explores strategies to improve engine efficiency through innovative materials, design concepts, and alternative energy sources. It highlights the use of nanomaterials and surface engineering to create hydrophobic or other types…
We probed the charge transfer interaction between the amine-containing molecules: hydrazine, polyaniline and aminobutyl phosphonic acid, and carbon nanotube field effect transistors (CNTFETs). We successfully converted p-type CNTFETs to…
Transport through carbon nanotube (CNT) quantum dots (QDs) in a magnetic field is discussed. The evolution of the system from the ultraviolet to the infrared is analyzed; the strongly correlated (SC) states arising in the infrared are…
The role of artificial intelligence (AI) in material science and engineering (MSE) is becoming increasingly important as AI technology advances. The development of high-performance computing has made it possible to test deep learning (DL)…
Molecular dynamics (MD) simulations with Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) force fields were conducted to determine the transversely isotropic elastic properties of carbon nanotubes (CNTs) containing vacancies.…
While particle beam steering (and in particular, "scraping") in accelerators by bent channeling crystals is an established technique extensively tested at IHEP Protvino and other major high-energy labs, an interesting question is how one…
Interacting defect systems are ubiquitous in materials under realistic scenarios, yet gaining an atomic-level understanding of these systems from a computational perspective is challenging - it often demands substantial resources due to the…
Machine learning offers an unprecedented perspective for the problem of classifying phases in condensed matter physics. We employ neural-network machine learning techniques to distinguish finite-temperature phases of the strongly correlated…
Directional detection of Dark Matter (DM) particles could be accomplished by studying either ion or electron recoils in large arrays of parallel carbon nanotubes. For instance, a MeV mass DM particle could scatter off a lattice electron,…
Interatomic potentials learned using machine learning methods have been successfully applied to atomistic simulations. However, accurate models require large training datasets, while generating reference calculations is computationally…
In recent experiments, unprecedentedly large values for the conductivity of electrolytes through carbon nanotubes (CNTs) have been measured, possibly owing to flow slip and a high pore surface charge density whose origin is still unknown.…
The capabilities of the mechanical resonator-based nanosensors in detecting ultra-small mass or force shifts have driven a continuing exploration of the palette of nanomaterials for such application purpose. Based on large-scale molecular…
We present a novel micromechanics-based phase field approach to model crack initiation and propagation in carbon nanotube (CNT) based composites. The constitutive mechanical and fracture properties of the nanocomposites are first estimated…
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…
The electrical and optical response of a field-effect device comprising a network of semiconductor-enriched single-wall carbon nanotubes, gated with sodium chloride solution is investigated. Field-effect is demonstrated in a device that…
In this paper we propose a one-dimensional convolutional neural network (CNN)-based state of charge estimation algorithm for electric vehicles. The CNN is trained using two publicly available battery datasets. The influence of different…
Tantalum nitride (TaN) has attracted considerable attention due to its unique electronic and thermal properties, high thermal conductivity, and applications in electronic components. However, for the {\theta}-phase of TaN, significant…
By sequential feeding of catalyst materials, it is revealed that the active growth sites are at the bottom of the carbon nanotubes (CNTs), and that catalyst particles are constantly encapsulated into nanotubes from the bottom. This gives a…
We present a new approach for predictive modeling and its uncertainty quantification for mechanical systems, where coarse-grained models such as constitutive relations are derived directly from observation data. We explore the use of a…
The advancements of nanomaterials or nanostructures have enabled the possibility of fabricating multifunctional materials that hold great promises in engineering applications. The carbon nanotube (CNT)-based nanostructure is one…