Related papers: Dissertation Machine Learning in Materials Science…
Carbon nanotube (CNT) is expected for much more important and broader applications in the future, because of its amazing electrical and mechanical properties. However, today, the prospect is detained by the fact that the growth of CNTs…
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…
Magnetic nanotubes have been widely studied because they are promising candidates to be part in devices based on spintronic and magnonic technologies. However, the experimental techniques used to prepare these elements could not guarantee…
We performed studies of coherent electronic transport through a single walled carbon nanotube. In the calculations multiple scattering on the contacts and interference processes were taken into account. Conductance is a composition of…
The effects of water wetting conditions on the transport properties of molecular nano-junctions are investigated theoretically by using a combination of classical molecular dynamics and first principles electronic transport calculations.…
Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the cost of calculating the adsorption…
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…
This study presents a simulation approach for three-dimensional nanotube networks using cubic and tetragonal unit cells to enhance modeling efficiency. A random-walk algorithm was developed to generate these networks, which were analyzed in…
In this paper, we analyze the effectiveness of transfer learning on classifying electronic components. Transfer learning reuses pre-trained models to save time and resources in building a robust classifier rather than learning from scratch.…
We report strategies of achieving both high assembly yield of carbon nanotubes at selected position of the circuit via dielectrophoresis (DEP) and field effect transistor (FET) yield using semiconducting enriched single walled carbon…
In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This…
Narrow carbon nanotubes (CNTs) desalinate water, mimicking water channels of biological membranes, yet the physics behind selectivity, especially, the effect of the membrane embedding CNTs on water and ion transfer, is still unclear. Here,…
The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant…
A vertical partial gate carbon nanotube (CNT) field-effect transistor (FET), which is amenable to the vertical CNT growth process and offers the potential for a parallel CNT array channel, is simulated using a self-consistent atomistic…
These brief lecture notes cover the basics of neural networks and deep learning as well as their applications in the quantum domain, for physicists without prior knowledge. In the first part, we describe training using backpropagation,…
We have fabricated air-stable n-type, ambipolar carbon nanotube field effect transistors (CNFETs), and used them in nanoscale memory cells. N-type transistors are achieved by annealing of nanotubes in hydrogen gas and contacting them by…
We investigate the electronic transport properties of semiconducting ($m$,$n$) carbon nanotubes (CNTs) on the mesoscopic length scale with arbitrarily distributed realistic defects. The study is done by performing quantum transport…
Graphene/carbon-nanotube (CNT) hybrid material can be useful in energy storage and nanoelectronic technologies. Here we address the CNT-oscillator encapsulated in a graphene-nanoribbon (GNR) trench as a novel design, and investigate its…
In the realm of quantum-effect devices and materials, two-dimensional electron gases (2DEGs) stand as fundamental structures that promise transformative technologies. However, the presence of impurities and defects in 2DEGs poses…
We exploit the near field enhancement of nano-antennas to investigate the Raman spectra of otherwise not optically detectable carbon nanotubes (CNTs). We demonstrate that a top-down fabrication approach is particularly promising when…