相关论文: A Machine Learning Potential for Graphene

We compare the three most commonly used scanning probe techniques to obtain a reliable value of the work function in graphene domains of different thickness. The surface potential (SP) of graphene is directly measured in Hall bar geometry…

The negative Poisson`s ratio (NPR) is a novel property of materials, which enhances the mechanical feature and creates a wide range of application prospects in lots of fields, such as aerospace, electronics, medicine, etc. Fundamental…

In order to realise the potential of graphene nanocomposites it is vital to control the degree of dispersion and achieve a strong graphene polymer interface. Herein, we developed a facile grafting to functionalisation approach for graphene…

We investigated theoretically the phonon thermal conductivity of single layer graphene. The phonon dispersion for all polarizations and crystallographic directions in graphene lattice was obtained using the valence-force field method. The…

We measure the temperature distribution in a biased single-layer graphene transistor using Raman scattering microscopy of the 2D-phonon band. Peak operating temperatures of 1050 K are reached in the middle of the graphene sheet at 210 KW…

We develop and compare four interatomic potentials for iron: a simple machine-learned embedded atom method (EAM) potential, a potential with machine-learned two- and three-body-dependent terms, a potential with machine-learned EAM and…

We investigate the melting phenomena of pristine, free-standing infinite and finite size graphene sheets via molecular dynamics simulation using AIREBO potential as implemented in the LAMMPS package. In our simulations, the temperature of…

Metal-organic Frameworks (MOFs) have emerged as potential candidates for direct air capture (DAC) of green house gases and water. Thermal properties of MOFs, such as their heat capacity, are used to determine the energy penalty associated…

We report the enhancement of the thermoelectric power (TEP) in graphene with extremely low disorder. At high temperature we observe that the TEP is substantially larger than the prediction of the Mott relation, approaching to the…

In this article we perform the quantization of graphene plasmons using both a macroscopic approach based on the classical average electromagnetic energy and a quantum hydrodynamic model, in which graphene charge carriers are modeled as a…

The mechanical properties of two-dimensional materials are important for a wide range of applications including composite and van der Waals-materials, flexible electronics and superconductivity. Several aspects are highly debated in the…

The thermal properties of graphitic ribbon are investigated based on Brenner's empirical potential. The reliability and usefulness of the empirical potential to address the thermal properties of covalent-bonded carbon nanostructures are…

The mobility of graphene is very high because the quantum Hall effects can be observed even at room temperature. Graphene has the potential of the material for novel devices because of this high mobility. But the energy gap of graphene is…

We investigate the thermal conductivity of suspended graphene as a function of the density of defects, ND, introduced in a controllable way. Graphene layers are synthesized using chemical vapor deposition, transferred onto a transmission…

Machine-learned interatomic potentials (MLIPs) promise to provide near density-functional theory accuracy at a fraction of the computational cost, offering a transformative route toward genuinely predictive chemistry. Yet their predictive…

We calculate the potential energy surfaces for graphene adsorbed on Cu(111), Ni(111), and Co(0001) using density functional theory and the Random Phase Approximation (RPA). For these adsorption systems covalent and dispersive interactions…

An analytical method for diffraction of a plane electromagnetic wave at periodically-modulated graphene sheet is presented. Both interface corrugation and periodical change in the optical conductivity are considered. Explicit expressions…

Machine learning and optimization algorithms have been widely applied in the design and optimization for photonic devices. In this article, we briefly review recent progress of this field of research and show some data-driven applications…

Although the pure component vapor pressure is one of the most important properties for designing chemical processes, no broadly applicable, sufficiently accurate, and open-source prediction method has been available. To overcome this, we…

The two-dimensionality of graphene and other layered materials can be exploited to simplify the theoretical description of their plasmonic and polaritonic modes. We present an analytical theory that allows us to simulate these excitations…