相关论文: A Machine Learning Potential for Graphene

Density functional theory (DFT) and thermal DFT (thDFT) calculations were used to evaluate the energy band structure, bandgap, and the total energy of various graphene quantum dots (GQDs). The DFT calculations were performed using local…

The maturity of the chemical vapor deposition graphene-based device processing has increased from chip level demonstrations to wafer-scale fabrication in the past few years. Due to this wafer-scale, electrical characterization and analysis…

In this work, we present a performance analysis of Field Effect Transistors based on recently fabricated 100% hydrogenated graphene (the so-called graphane) and theoretically predicted semi-hydrogenated graphene (i.e. graphone). The…

Predicting material properties has always been a challenging task in materials science. With the emergence of machine learning methodologies, new avenues have opened up. In this study, we build upon our recently developed Graph Neural…

The unique properties of graphene offer immense opportunities for applications to many scientific fields, as well as societal needs, beyond our present imagination. One of the important features of graphene is the relatively simple…

In addition to its exotic electronic properties graphene exhibits unusually high intrinsic thermal conductivity. The physics of phonons - the main heat carriers in graphene - was shown to be substantially different in two-dimensional (2D)…

There has been an ongoing race for the past several years to develop the best universal machinelearning interatomic potential. This progress has led to increasingly accurate models for predictingenergy, forces, and stresses, combining…

In this paper, we present the extended dissipaton theory, including the dissipaton-equation-of-motion formalism and the equivalent dissipaton-embedded quantum master equation. These are exact, non-Markovian, and non-perturbative theories,…

Density functional theory (DFT) and many body perturbation theory at the G$_0$W$_0$ level are employed to study the electronic properties of polythiophene (PT) adsorbed on graphene surface. Analysis of charge density difference shows the…

Developing data-driven machine-learning interatomic potentials for materials containing many elements becomes increasingly challenging due to the vast configuration space that must be sampled by the training data. We study the learning…

Thermal and mechanical properties of two-dimensional nanomaterials are commonly studied by calculating force constants using the density functional theory (DFT) and classical molecular dynamics (MD) simulations. Although DFT simulations…

Graphene oxide (GO) exhibits rich chemical heterogeneity that strongly influences its structural, thermal, and mechanical properties, yet quantitatively linking reduction chemistry to heat transport remains challenging. In this work, we…

We present the interatomic force constants and phonon dispersions of graphite and graphene from the LCBOPII empirical bond order potential. We find a good agreement with experimental results, particularly in comparison to other bond order…

First-principles molecular dynamics simulations of heat transport in systems with large-scale structural features are challenging due to their high computational cost. Here, using polycrystalline graphene as a case study, we demonstrate the…

The theoretical investigation of gas adsorption, storage, separation, diffusion and related transport processes in porous materials relies on a detailed knowledge of the potential energy surface of molecules in a stationary environment. In…

The prediction of atomistic fracture mechanisms in body-centred cubic (bcc) iron is essential for understanding its semi-brittle nature. Existing atomistic simulations of the crack-tip deformation mechanisms under mode-I loading based on…

Graphane is a quasi-two-dimensional material consisting of a single layer of fully hydrogenated graphene, with a C:H ratio of 1. We study nuclear quantum effects in the so-called chair-graphane by using path-integral molecular dynamics…

In this work, we have studied the phonon properties of multi-layered graphene with the use of Molecular Dynamics (MD) simulations and the k-space Autocorrelation Sequence (k-VACS) method. We calculate the phonon dispersion curves, densities…

Phonons play a critical role in determining various material properties, but conventional methods for phonon calculations are computationally intensive, limiting their broad applicability. In this study, we present an approach to accelerate…

Metal-organic frameworks (MOFs) are nanoporous compounds composed of metal ions and organic linkers. MOFs play an important role in industrial applications such as gas separation, gas purification, and electrolytic catalysis. Important MOF…