Related papers: Identification and Structural Characterization of …
Twisted van der Waals materials have risen as highly tunable platform for realizing unconventional superconductivity. Here we demonstrate how a topological superconducting state can be driven in a twisted graphene multilayer at a twist…
Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit certain stationarity properties in time such as smoke, vegetation and fire. The analysis of DT is important for recognition, segmentation, synthesis or retrieval…
Semiconductor research has shifted towards exploring two-dimensional (2D) materials as candidates for next-generation electronic devices due to the limitations of existing silicon technology. Transition Metal Dichalcogenides (TMDCs) stand…
The deformation of monolayer graphene, produced by chemical vapor deposition (CVD), on a polyester film substrate has been investigated through the use of Raman spectroscopy. It has been found that the microstructure of the CVD graphene…
We present a quick and reliable method to cut 2D materials for creating 2D twisted heterostructures and devices. We demonstrate the effectiveness of using a tapered fibre scalped for cutting graphene. Electrical transport measurements show…
Deep learning has been used as a powerful tool for various tasks in computer vision, such as image segmentation, object recognition and data generation. A key part of end-to-end training is designing the appropriate encoder to extract…
We report planar tunneling spectroscopy measurements on metal-WSe$_2$-twisted bilayer graphene heterostructures across a broad range of gate and bias voltages. The observed experimental features are attributed to phonon-assisted tunneling…
Deep learning has introduced several learning-based methods to recognize breast tumours and presents high applicability in breast cancer diagnostics. It has presented itself as a practical installment in Computer-Aided Diagnostic (CAD)…
Two-dimensional (2D) layered materials, demonstrating significantly different properties from their bulk counterparts, offer a materials platform with potential applications from energy to information processing devices. Although some…
Second-order superlattices form when moir\'e superlattices of similar periodicities interfere with each other, leading to even larger superlattice periodicities. These crystalline structures have been engineered utilizing two-dimensional…
Deep learning, as a promising new area of machine learning, has attracted a rapidly increasing attention in the field of medical imaging. Compared to the conventional machine learning methods, deep learning requires no hand-tuned feature…
We report an interpretation method for deep learning models that allows us to handle high-dimensional spectral data in materials science. The proposed method uses feature extraction and clustering analysis to categorize materials into…
The prediction of upcoming events in industrial processes has been a long-standing research goal since it enables optimization of manufacturing parameters, planning of equipment maintenance and more importantly prediction and eventually…
Artificially constructed van der Waals heterostructures (vdWHs) provide an ideal platform for realizing emerging quantum phenomena in condensed matter physics. Two methods for building vdWHs have been developed: stacking two-dimensional…
Within the world of machine learning there exists a wide range of different methods with respective advantages and applications. This paper seeks to present and discuss one such method, namely Convolutional Neural Networks (CNNs). CNNs are…
Focusing on the twist angle for the minimal commensurate structure, we perform nonperturbative calculations of electron dynamics in the twisted bilayer graphene (TBG) under intense laser fields. We show that the TBG exhibits enriched…
Atomically thin transition metal dichalcogenides have emerged as promising candidates for sensitive photodetection. Here, we report a photoconductivity study of biased mono- and bilayer molybdenum disulfide field-effect transistors. We…
Transition metal dichalcogenides (TMDs) have recently attracted attention due to their interesting electronic and optical properties. Fabrication of these materials in a reliable and facile method is important for future applications, as…
Cell detection in microscopy images is important to study how cells move and interact with their environment. Most recent deep learning-based methods for cell detection use convolutional neural networks (CNNs). However, inspired by the…
In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image classification, object detection, and image segmentation. There are numerous types…