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We show that graphene-dielectric multilayers give rise to an unusual tunability of the Casimir-Lifshitz forces, and allow to easily realize completely different regimes within the same structure. Concerning thermal effects,…

Mesoscale and Nanoscale Physics · Physics 2017-03-27 Chahine Abbas , Brahim Guizal , Mauro Antezza

Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics (MD) simulations reveal the atomistic scale mechanisms, so they are crucial for the understanding of the macroscopic property deterioration of…

Materials Science · Physics 2022-10-19 XiaoYang Wang , YiNan Wang , LinFeng Zhang , FuZhi Dai , Han Wang

The specifics of charge screening and electrostatic potential spatial distribution in multilayered graphene films placed in between charged substrates is theoretically analyzed. It is shown that by varying the areal charge densities on the…

Mesoscale and Nanoscale Physics · Physics 2014-03-21 Natalya A. Zimbovskaya , Eugene J. Mele

Besides the plenty of applications of graphene allotropes in condensed matter and nanotechnology, we argue that graphene sheets might be engineered to support room-temperature topological quantum processing of information. The argument is…

Quantum Physics · Physics 2012-09-13 Annalisa Marzuoli , Giandomenico Palumbo

A high dimensional artificial neural network interatomic potential for Mo is developed. To train and validate the potential density functional theory calculations on structures and properties that correlate to fracture, such as elastic…

Materials Science · Physics 2021-12-10 Masud Alam , Liverios Lymperakis

Machine-learning interatomic potential models based on graph neural network architectures have the potential to make atomistic materials modeling widely accessible due to their computational efficiency, scalability, and broad applicability.…

Materials Science · Physics 2024-11-05 Çetin Kılıç , Sümeyra Güler-Kılıç

Graph neural networks (GNNs) integrate deep architectures and topological structure modeling in an effective way. However, the performance of existing GNNs would decrease significantly when they stack many layers, because of the…

Machine Learning · Computer Science 2021-07-07 Kaixiong Zhou , Xiao Huang , Daochen Zha , Rui Chen , Li Li , Soo-Hyun Choi , Xia Hu

Graph Neural Networks (GNNs) show strong expressive power on graph data mining, by aggregating information from neighbors and using the integrated representation in the downstream tasks. The same aggregation methods and parameters for each…

Machine Learning · Computer Science 2022-03-22 Xiaojun Ma , Qin Chen , Yuanyi Ren , Guojie Song , Liang Wang

The deposition of dielectric materials on graphene is one of the bottlenecks for unlocking the potential of graphene in electronic applications. In this paper we demonstrate the plasma enhanced atomic layer deposition of 10 nm thin high…

The thermal stability of graphene/graphane nanoribbons (GGNRs) is investigated using density functional theory. It is found that the energy barriers for the diffusion of hydrogen atoms on the zigzag and armchair interfaces of GGNRs are 2.86…

Materials Science · Physics 2015-03-17 Z. M. Ao , A. D. Hernández-Nieves , F. M. Peeters , S. Li

A monolayer of graphene irradiated with circularly polarized light suggests a unique platform for surface electromagnetic wave (plasmon-polariton) manipulation. In fact, the time periodicity of the Hamiltonian leads to a geometric…

Mesoscale and Nanoscale Physics · Physics 2015-02-19 Dmitry Yudin , Olle Eriksson , Mikhail I. Katsnelson

We explore ways in which the close proximity between graphene sheets and monolayers of 2D superconductors can lead to hybridization between their collective excitations. We consider heterostructures formed by combinations of graphene sheets…

Mesoscale and Nanoscale Physics · Physics 2022-01-11 A. T. Costa , N. M. R. Peres

To accelerate the process of materials design, materials science has increasingly used data driven techniques to extract information from collected data. Specially, machine learning (ML) algorithms, which span the ML discipline, have…

In this review, we provide an in-depth description of the physics of monolayer and bilayer graphene from a theorist's perspective. We discuss the physical properties of graphene in an external magnetic field, reflecting the chiral nature of…

Materials Science · Physics 2014-11-20 D. S. L. Abergel , V. Apalkov , J. Berashevich , K. Ziegler , Tapash Chakraborty

Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing and learning representations from graph-structured data. A crucial prerequisite for the outstanding performance of GNNs is the availability of complete graph…

Machine Learning · Computer Science 2024-08-12 Peng Yuan , Peng Tang

Graphene and graphene-based materials exhibit exceptional optical and electrical properties with great promise for novel applications in light detection. However, several challenges prevent the full exploitation of these properties in…

Materials Science · Physics 2018-09-20 Adolfo De Sanctis , Jake D. Mehew , Monica F. Craciun , Saverio Russo

Two rich and vibrant fields of investigation, graphene physics and plasmonics, strongly overlap. Not only does graphene possess intrinsic plasmons that are tunable and adjustable, but a combination of graphene with noble-metal…

Mesoscale and Nanoscale Physics · Physics 2013-01-21 A. N. Grigorenko , Marco Polini , K. S. Novoselov

Graphene is an emerging class of two-dimensional (2D) material with unique electrical properties and a wide range of potential practical applications. In addition, graphene hybrid structures combined with other 2D materials, metal…

Materials Science · Physics 2014-01-03 Xu-Dong Chen , Zhi-Bo Liu , Wen-Shuai Jiang , Xiao-Qing Yan , Fei Xing , Peng Wang , Yongsheng Chen , Jian-Guo Tian

Double-gated graphene devices provide an important platform for understanding electrical and optical properties of graphene. Here we present transport measurements of single layer, bilayer and trilayer graphene devices with suspended top…

Mesoscale and Nanoscale Physics · Physics 2015-06-05 J. Velasco , Y. Lee , L. Jing , G. Liu , W. Bao , C. N. Lau

$\alpha$-graphyne is a two-dimensional sheet of $sp$-$sp^2$ hybridized carbon atoms in a honeycomb lattice. While the geometrical structure is similar to that of graphene, the hybridized triple bonds give rise to electronic structure that…

Materials Science · Physics 2017-08-04 Hyeondeok Shin , Jeongnim Kim , Hoonkyung Lee , Olle Heinonen , Anouar Benali , Yongkyung Kwon
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