相关论文: A Machine Learning Potential for Graphene

Machine learning interatomic potentials (MLIPs) based on a large dataset obtained by density functional theory (DFT) calculation have been developed recently. This study gives both conceptual and practical bases for the high accuracy of…

Machine-learning interatomic potentials are widely used as computationally efficient surrogates for density functional theory in atomistic simulations, enabling large-scale, long-time modeling of materials systems. We investigate how…

We present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress…

This study reports the successful synthesis of multilayered graphene sheets via microwave atmospheric pressure plasma. This innovative approach streamlines and expedites graphene production and other carbon nanostructures, eliminating the…

We obtained numerical and closed-form analytic expressions for finite-temperature plasmon dispersion relations for intrinsic graphene in the presence of a finite energy gap in the energy spectrum. The calculations were carried out using the…

In this article, we investigate the temperature and chemical potential dependence of the optical conductivity of graphene, within a field theoretical representation in the continuum approximation, arising from an underlying tight-binding…

The random phase approximation (RPA) is attracting renewed interest as a universal and accurate method for first-principles total energy calculations. The RPA naturally accounts for long-range dispersive forces without compromising accuracy…

A thorough understanding of the electronic structure is a necessary first step for the design of nanoelectronics, chemical/bio-sensors, electrocatalysts, and nanoplasmonics using graphene. As such, theoretical spectroscopic techniques to…

Ab-initio density functional perturbation theory (DFPT) has been employed to study thermodynamical properties of pure and doped graphene sheet and the results have been compared with available theoretical and experimental data. The…

In this work, we present a comprehensive investigation of graphene's thermal conductivity using first-principles density functional perturbation theory calculations, with a focus on the phonon and lattice vibrational properties underlying…

Material characterization in nano-mechanical tests requires precise interatomic potentials for the computation of atomic energies and forces with near-quantum accuracy. For such purposes, we develop a robust neural-network interatomic…

Far infrared(FIR) data on the optical properties of graphite are presently lacking. An important step towards filling this gap was taken by Kuzmenko et al. (2008) who measured, on HOPG (Highly Oriented Pyrolitic Graphite) at normal…

A Spectral Neighbor Analysis (SNAP) machine learning interatomic potential (MLIP) has been developed for simulations of carbon at extreme pressures (up to 5 TPa) and temperatures (up to 20,000 K). This was achieved using a large database of…

Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management. In this paper, we present a…

The theory of optical effects in hydrogenated graphene (graphane) in terahertz and infrared range is developed, including the analysis of complex conductivity, reflection coefficient for graphane on a substrate and dispersion of surface…

While traditional trial-and-error methods for designing amorphous alloys are costly and inefficient, machine learning approaches based solely on composition lack critical atomic structural information. Machine learning interatomic…

We introduce a machine-learning interatomic potential for tungsten using the Gaussian Approximation Potential framework. We specifically focus on properties relevant for simulations of radiation-induced collision cascades and the damage…

Molecular dynamics simulations provide a versatile framework to study interfacial heat transport, but their accuracy remains limited by the accuracy of available interatomic potentials. In the past, researchers have adopted the use of…

Accurate atomistic simulations of gas-surface scattering require potential energy surfaces that remain reliable over broad configurational and energetic ranges while retaining the efficiency needed for extensive trajectory sampling. Here,…

Metallenes are atomically thin, nonlayered two-dimensional materials. While they have appealing properties, their isotropic metallic bonding makes their stabilization difficult and presents considerable challenges to their synthesis and…