相关论文: Mesostructure of graphite composite and its lifeti…

Incorporating heteroatoms into the graphene lattice may be used to tailor its electronic, mechanical and chemical properties. Direct substitutions have thus far been limited to incidental Si impurities and P, N and B dopants introduced…

Mesh models are a promising approach for encoding the structure of 3D objects. Current mesh reconstruction systems predict uniformly distributed vertex locations of a predetermined graph through a series of graph convolutions, leading to…

Graphene, being one-atom thick, is extremely sensitive to the presence of adsorbed atoms and molecules and, more generally, to defects such as vacancies, holes and/or substitutional dopants. This property, apart from being directly usable…

The processes of constructing some graphs from others using binary operations of union with intersection (gluing) are studied. For graph classes closed with respect to gluing operations the elemental and operational bases are introduced.…

Results of first principles modelling of interactions graphene and graphite with iron impurities predict the colossal difference between these two carbon allotropes. Insertion of the iron atoms between the planes of graphite is much more…

We introduce the idea that the electronic band structure of a charge density wave system may mimic the electronic structure of graphene. In that case a class of materials quite different from graphene might be opened up to exploit…

Doping of the graphene lattice with transition metal atoms resulting in high magnetic anisotropy energy (MAE) is an important goal of materials research owing to its potential application in spintronics. In this article, by using…

Several topics concerning nuclear structure and electromagnetic interactions of heavy nuclei are reviewed. These comprehend the deformed single-particle shell model, nuclear collective motion, symmetry breaking and approximate symmetry…

Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases. In this work, we study feature learning techniques for graph-structured inputs. Our starting point is…

Graphene, a single atomic layer of graphite, first isolated in 2004, has made a quantum leap in the exploration of the physics of two-dimensional electron systems. Since the initial report of its discovery, many thousands of papers have…

This review systematically analyzes patent disclosures regarding plasmonic structures, devices, and integrated applications, highlighting the technology's capability to confine and manipulate electromagnetic energy at the nanoscale. Core…

Compressed hydrogen passes through a series of layered structures in which the layers can be viewed as distorted graphene sheets. The electronic structures of these layered structures can be understood by studying simple model systems- an…

The recent discovery of graphene has sparked significant interest, which has so far been focused on the peculiar electronic structure of this material, in which charge carriers mimic massless relativistic particle. However, the structure of…

Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image data to match the available machine learning architectures,…

The past two decades have witnessed tremendous progress in the microscopic description of atomic nuclei. The Topical Review `The Future of Nuclear Structure' aims at summarizing the current state-of-the-art microscopic calculations in…

Graphene is a rapidly rising star on the horizon of materials science and condensed matter physics. This strictly two-dimensional material exhibits exceptionally high crystal and electronic quality and, despite its short history, has…

Deep neural networks for graphs have emerged as a powerful tool for learning on complex non-euclidean data, which is becoming increasingly common for a variety of different applications. Yet, although their potential has been widely…

We experimentally investigated the properties of graphite layers produced by an easy and non-conventional method of repeatedly rubbing conventional random stacked graphite bulk against insulating and semiconductor substrates. The patterned…

Applications of machine learning techniques in materials science are often based on two key ingredients, a set of empirical descriptors and a database of a particular material property of interest. The advent of graph neural networks, such…

Computer experiments concerning interactions between a graphite surface and the rigid pyramidal nanoasperity of a friction force microscope tip when it is brought close to and retracted from the graphitic sample are presented. Covalent…