Edge Saturation effects on the magnetism and band gaps in multilayer graphene ribbons and flakes

Using a density functional theory based electronic structure method and semi-local density approximation, we study the interplay of geometric confinement, magnetism and external electric fields on the electronic structure and the resulting band gaps of multilayer graphene ribbons whose edges are saturated with molecular hydrogen (H$_2$) or hydroxyl (OH) groups. We discuss the similarities and differences of computed features in comparison with the atomic hydrogen (or H-) saturated ribbons and flakes. For H$_2$ edge-saturation, we find \emph{shifted} labeling of three armchair ribbon classes and magnetic to non-magnetic transition in narrow zigzag ribbons whose critical width changes with the number of layers. Other computed characteristics, such as the existence of a critical gap and external electric field behavior, layer dependent electronic structure, stacking-dependent band gap induction and the length confinement effects remain qualitatively same with those of H-saturated ribbons.Comment: 9 pages, 10 figures, submitte

Parallel Algorithms Align with Neural Execution

Neural algorithmic reasoners are parallel processors. Teaching them sequential algorithms contradicts this nature, rendering a significant share of their computations redundant. Parallel algorithms however may exploit their full computational power, therefore requiring fewer layers to be executed. This drastically reduces training times, as we observe when comparing parallel implementations of searching, sorting and finding strongly connected components to their sequential counterparts on the CLRS framework. Additionally, parallel versions achieve strongly superior predictive performance in most cases.Comment: 8 pages, 5 figures, To appear at the KLR Workshop at ICML 202

Tuning the electronic structure of graphene by ion irradiation

Mechanically exfoliated graphene layers deposited on SiO2 substrate were irradiated with Ar+ ions in order to experimentally study the effect of atomic scale defects and disorder on the low-energy electronic structure of graphene. The irradiated samples were investigated by scanning tunneling microscopy and spectroscopy measurements, which reveal that defect sites, besides acting as scattering centers for electrons through local modification of the on-site potential, also induce disorder in the hopping amplitudes. The most important consequence of the induced disorder is the substantial reduction in the Fermi velocity, revealed by bias-dependent imaging of electron-density oscillations observed near defect sites

Ultra-flat twisted superlattices in 2D heterostructures

Moire-superlattices are ubiquitous in 2D heterostructures, strongly influencing their electronic properties. They give rise to new Dirac cones and are also at the origin of the superconductivity observed in magic-angle bilayer graphene. The modulation amplitude (corrugation) is an important yet largely unexplored parameter in defining the properties of 2D superlattices. The generally accepted view is that the corrugation monotonically decreases with increasing twist angle, while its effects on the electronic structure diminish as the layers become progressively decoupled. Here we found by lattice relaxation of around 8000 different Moire-superstructures using high scale Classical Molecular Simulations combined with analytical calculations, that even a small amount of external strain can substantially change this picture, giving rise to more complex behavior of superlattice corrugation as a function of twist angle. One of the most surprising findings is the emergence of an ultra-flat phase that can be present for arbitrary small twist angle having a much lower corrugation level than the decoupled phase at large angles. Furthermore, Moire-phase maps evidence that the state with no external strain is located in the close vicinity of a triple Moire-phase boundary, implying that very small external strain variations can cause drastic changes in the realized superlattice morphology and corrugation. This renders the practical realization of 2D heterostructures with large-area homogeneous superlattice morphology highly challenging

Different sensing mechanisms in single wire and mat carbon nanotubes chemical sensors

Chemical sensing properties of single wire and mat form sensor structures fabricated from the same carbon nanotube (CNT) materials have been compared. Sensing properties of CNT sensors were evaluated upon electrical response in the presence of five vapours as acetone, acetic acid, ethanol, toluene, and water. Diverse behaviour of single wire CNT sensors was found, while the mat structures showed similar response for all the applied vapours. This indicates that the sensing mechanism of random CNT networks cannot be interpreted as a simple summation of the constituting individual CNT effects, but is associated to another robust phenomenon, localized presumably at CNT-CNT junctions, must be supposed.Comment: 12 pages, 5 figures,Applied Physics A: Materials Science and Processing 201

Neural Algorithmic Reasoning for Combinatorial Optimisation

Solving NP-hard/complete combinatorial problems with neural networks is a challenging research area that aims to surpass classical approximate algorithms. The long-term objective is to outperform hand-designed heuristics for NP-hard/complete problems by learning to generate superior solutions solely from training data. The Travelling Salesman Problem (TSP) is a prominent combinatorial optimisation problem often targeted by such approaches. However, current neural-based methods for solving TSP often overlook the inherent "algorithmic" nature of the problem. In contrast, heuristics designed for TSP frequently leverage well-established algorithms, such as those for finding the minimum spanning tree. In this paper, we propose leveraging recent advancements in neural algorithmic reasoning to improve the learning of TSP problems. Specifically, we suggest pre-training our neural model on relevant algorithms before training it on TSP instances. Our results demonstrate that, using this learning setup, we achieve superior performance compared to non-algorithmically informed deep learning models