Related papers: Dissertation Machine Learning in Materials Science…
We develop theoretical arguments that demonstrate the possibility of metallic field-effect transistors (METFET's) in one-dimensional systems and particularly in armchair carbon nanotubes. A very inhomogeneous electric field, such as the…
Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the…
Building smaller transistors with enhanced functionality is critical in extending the limits of Moores law and meeting the demands of the electronics industry. Here we demonstrate transistor operation in a suspended single carbon nanotube…
State-of-the-art carbon nanotube field-effect transistors (CNFETs) behave as Schottky barrier (SB)-modulated transistors. It is known that vertical scaling of the gate oxide significantly improves the performance of these devices. However,…
The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of…
Spin-dependent coherent quantum transport through carbon nanotubes (CNT) is studied theoretically within a tight-binding model and the Green's function partitioning technique. End-contacted metal/nanotube/metal systems are modelled and next…
Carbon nanotubes play a fundamental role in the rapidly developing field of nanoscience and nanotechnology because of their unique properties and high potential applictions. In this article, the different synthesis method of CNTs are…
By developing a two-dimensional (2D) full quantum simulation, the attributes of carbon nanotube field-effect transistors (CNTFETs) in different temperatures have been comprehensively investigated. Simulations have been performed by…
This research aims to explore the application of deep learning in autonomous driving computer vision technology and its impact on improving system performance. By using advanced technologies such as convolutional neural networks (CNN),…
The performance of carbon nanotube (CNT) cables, a contender for copper-wire replacement, is tied to its metallic and semi-conducting-like conductivity responses with temperature; the origin of the semi-conducting-like response however is…
Machine learning has recently been applied to many problems in condensed matter physics. A common point of many proposals is to save computational cost by training the machine with data from a simple example and then using the machine to…
We investigated the accelerated prediction of the thermal conductivity of materials through end- to-end structure-based approaches employing machine learning methods. Due to the non-availability of high-quality thermal conductivity data, we…
We use computational screening to systematically investigate the use of transition metal doped carbon nanotubes for chemical gas sensing. For a set of relevant target molecules (CO, NH3, H2S) and the main components of air (N2, O2, H2O), we…
Carbon Nanotube (CNT) is one of the most significant materials for the development of faster and improved performance of nano-scaled transistors. This paper aims at analyzing a trade-off between device performance and device size of CNT…
The material science literature contains up-to-date and comprehensive scientific knowledge of materials. However, their content is unstructured and diverse, resulting in a significant gap in providing sufficient information for material…
Machine learning algorithms have recently emerged as a tool to generate force fields which display accuracies approaching the ones of the ab-initio calculations they are trained on, but are much faster to compute. The enhanced computational…
Carbon nanotubes (CNTs) have attracted increasing attention because of their unique structural, mechanical, and electronic properties. Surface chemistry modifications are also useful and critical to manipulate the adsorptive properties of…
Atomistic density functional theory (DFT) calculations of the capacitance between a metallic cylindric gate and a carbon nanotube (CNT) are reported. Results stressing the predominant effect of quantum capacitance in limiting or even…
We report on a new computational model to efficiently simulate carbon nanotubebased field effect transistors (CNT-FET). In the model, a central region is formed by a semiconducting nanotube that acts as the conducting channel, surrounded by…
Carbon nitride research has reached a promising point in today's research endeavours with diverse applications including photocatalysis, energy storage, and sensing due to their unique electronic and structural properties. Recent advances…