Related papers: Dissertation Machine Learning in Materials Science…
Carbon nanotube networks are one of the candidate materials to function as malleable, transparent, conducting films, with the technologically promising application of being used as flexible electronic displays. Nanotubes disorderly…
Carbon nanotubes have emerged as a possible new material for electronic applications. They show promising characteristics for transistors as well as for interconnects. Here we review their basic properties and focus on the status of…
As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure. Given the importance of process-structure-property relations…
The atomistic simulations of carbon nanotube (CNT) - carbon reinforced composite material are reported. The studied composite samples were obtained by impregnating certain amounts of CNTs (3,3) and (6,6) into pristine graphite matrix. The…
In this study, a model of a Schottky-barrier carbon nanotube field- effect transistor (CNT-FET), with ferromagnetic contacts, has been developed. The emphasis is put on analysis of current-voltage characteristics as well as shot (and…
Corrosion is an inevitable phenomenon that is inherent in metals and even though there has been significant research on this subject, no ideal protection has been discovered to fully prevent corrosion. However, methods such as using…
Random networks of single-walled carbon nanotubes (CNTs) usually contain both metallic (m-CNTs) and semiconducting (s-CNTs) nanotubes with an approximate ratio of 1:2, which leads to a trade-off between on-conductance and on/off ratio. We…
Quantum matter, the research field studying phases of matter whose properties are intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter physics, materials science, statistical mechanics, quantum information,…
We have theoretically investigated the thermoelectric properties of impurity-doped one-dimensional semiconductors, focusing on nitrogen-substituted (N-substituted) carbon nanotubes (CNTs), using the Kubo formula combined with a…
Artificial intelligence and machine learning paves the way to achieve greater technical feats. In this endeavor to hone these techniques, quantum machine learning is budding to serve as an important tool. Using the techniques of deep…
High-performance solution-processed short-channel carbon nanotube (CNT) thin film transistors (TFTs) are fabricated using densely aligned arrays of metallic CNTs (m-CNTs) as source and drain electrodes, and aligned arrays of semiconducting…
Materials characterization remains a significant, time-consuming undertaking. Generally speaking, spectroscopic techniques are used in conjunction with empirical and ab-initio calculations in order to elucidate structure. These experimental…
The integration of machine learning techniques with triboelectric nanogenerators (TENGs) offers a transformative pathway for optimizing energy harvesting technologies. In this study, we propose a comprehensive framework that utilizes graph…
The influence of defects on electron transport in single-wall carbon nanotube field effect transistors (CNFETs) is probed by combined scanning gate microscopy (SGM) and scanning impedance microscopy (SIM). SGM reveals a localized field…
Machine learning models are increasingly used in many engineering fields thanks to the widespread digital data, growing computing power, and advanced algorithms. Artificial neural networks (ANN) is the most popular machine learning model in…
The application of TensorFlow pre-trained models in deep learning is explored, with an emphasis on practical guidance for tasks such as image classification and object detection. The study covers modern architectures, including ResNet,…
Nanotruss structures made of carbon nanotubes are investigated in two conceptual applications: either as building blocks of metamaterials or for nanostructural applications. The nanotrusses are optimized for different purposes, including…
Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build…
In this work we propose simple, effective and computationally efficient transfer learning approaches for structure-property relation predictions in the context of materials, with highly informative input from different modalities. As…
We present an empirical model for the nearballistic transport in carbon nanotube (CNT) transistors used as strain sensors. This model describes the intrinsic effect of strain on the transport in CNTs by taking into account phonon scattering…