Related papers: Dissertation Machine Learning in Materials Science…
Understanding structure-property relationships in complex materials requires integrating complementary measurements across multiple length scales. Here we propose an interpretable "multimodal" machine learning framework that unifies…
Carbon Nanotubes have shown to be an attractive option in the race to find a replacement to silicon-based transistors, due to its high electrical conductivity, extraordinary mechanical strength, and thermal conductivity. However, challenges…
The conductivity of carbon nanotube (CNT) networks can be improved markedly by doping with nitric acid. In the present work, CNTs and junctions of CNTs functionalized with NO$_3$ molecules are investigated to understand the microscopic…
Dielectric properties of carbon nanotubes (CNT) are calculated with taking into account the deviation of carbon valence electrons from to be placed exactly on the CNT surface. The results show difference to the usually applied 2D electron…
In this paper, we model the evolution and self-assembly of randomly oriented carbon nanotubes (CNTs), grown on a metallic substrate in the form of a thin film for field emission under diode configuration. Despite high output, the current in…
Carbon nanotubes (CNT) have been recently proposed as stabilizers against grain growth that can happen even at low temperature inputs in nano-crystalline and ultrafine-grained materials obtained by severe plastic deformation. In this study,…
Hardware imperfections in RF transmitters introduce features that can be used to identify a specific transmitter amongst others. Supervised deep learning has shown good performance in this task but using datasets not applicable to real…
In this work, we have implemented an accurate machine-learning approach for predicting various key analog and RF parameters of Negative Capacitance Field-Effect Transistors (NCFETs). Visual TCAD simulator and the Python high-level language…
Identification of important topics in a text can facilitate knowledge curation, discover thematic trends, and predict future directions. In this paper, we aim to quantitatively detect the most common research themes in the emerging…
The performance limits of the multilayer graphene nanoribbon (GNR) field-effect transistor (FET) are assessed and compared to those of monolayer GNR FET and carbon nanotube (CNT) FET. The results show that with a thin high-k gate insulator…
Substantial progress on field effect transistors "FETs" consisting of semiconducting single wall carbon nanotubes "s-SWNTs" without detectable traces of metallic nanotubes and impurities is reported. Nearly perfect removal of metallic…
Ambipolar carbon nanotube based field-effect transistors (AP-CNFETs) exhibit unique electrical characteristics, such as tri-state operation and bi-directionality, enabling systems with complex and reconfigurable computing. In this paper,…
Material classification in natural settings is a challenge due to complex interplay of geometry, reflectance properties, and illumination. Previous work on material classification relies strongly on hand-engineered features of visual…
We demonstrate solution processable large area field effect transistors (FETs) from aligned arrays of carbon nanotubes (CNTs). Commercially available, surfactant free CNTs suspended in aqueous solution were aligned between source and drain…
Carbon nanotube field-effect transistors (FETs) with passivated coaxial gate structures have been fabricated after growth of contacted suspended single wall nanotubes (SWNTs) and subsequent coating with gate dielectrics. Electron…
We show how machine learning techniques based on Bayesian inference can be used to reach new levels of realism in the computer simulation of molecular materials, focusing here on water. We train our machine-learning algorithm using…
Machine learning techniques have been widely employed as effective tools in addressing various engineering challenges in recent years, particularly for the challenging task of microstructure-informed materials modeling. This work provides a…
We report a method for probing electromechanical properties of multiwalled carbon nanotubes(CNTs). This method is based on AFM measurements on a doubly clamped suspended CNT electrostatically deflected by a gate electrode. We measure the…
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,…
This paper introduces a novel simulation tool for analyzing and training neural network models tailored for compute-in-memory hardware. The tool leverages physics-based device models to enable the design of neural network models and their…