相关论文: Machine Learning Potential for Hexagonal Boron Nit…

The emerging ferroelectric properties of two-dimensional (2D) heterostructures are at the forefront of science and prospective technology. In moir\'e bilayers, twisting or heterostructuring causes local atomic reconstruction, which even at…

The highly anisotropic thermal conductivity in layered materials is crucial for a broad range of applications such as thermal management of electronic devices, thermal insulation, and thermoelectrics. Understanding of anisotropic thermal…

A new interlayer force-field for layered hexagonal boron nitride (h-BN) based structures is presented. The force-field contains three terms representing the interlayer attraction due to dispersive interactions, repulsion due to anisotropic…

We present an accurate interatomic potential for graphene, constructed using the Gaussian Approximation Potential (GAP) machine learning methodology. This GAP model obtains a faithful representation of a density functional theory (DFT)…

Thermal management materials are of critical importance for engineering miniaturized electronic devices, where theoretical design of such materials demands the evaluation of thermal conductivities which are numerically expensive. In this…

Hexagonal boron nitride (hBN) is an emerging layered material that plays a key role in a variety of two-dimensional devices, and has potential applications in nanophotonics and nanomechanics. Here, we demonstrate the first cavity…

The electronic properties graphene nanoflakes (GNFs) with embedded hexagonal boron nitride (hBN) domains are investigated by combined {\it ab initio} density functional theory calculations and machine learning techniques. The energy gaps of…

We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid and amorphous elemental carbon. Based on a machine-learning representation of the density-functional theory (DFT) potential-energy surface, such…

Moir\'e superlattice of twisted hexagonal boron nitride (hBN) has emerged as an advanced atomically thin van der Waals interfacial ferroelectricity platform. Nanoscale periodic ferroelectric moir\'e domains with out-of-plane potentials in…

Using atomistic simulations we investigate the thermodynamical properties of a single atomic layer of hexagonal boron nitride (h-BN). The thermal induced ripples, heat capacity, and thermal lattice expansion of large scale h-BN sheets are…

Monolayer and multilayer graphene are promising materials for applications such as electronic devices, sensors, energy generation and storage, and medicine. In order to perform large-scale atomistic simulations of the mechanical and thermal…

Development of machine learned interatomic potentials (MLIP) is critical for performing reliable simulations of materials at length and time scales that are comparable to those in the laboratory. We present here a MLIP suitable for…

We develop a high-dimensional neural network potential (NNP) to describe the structural and energetic properties of borophene deposited on silver. This NNP has the accuracy of DFT calculations while achieving computational speedups of…

We show that hexagonal boron nitride (hBN), a two-dimensional insulator, when subjected to an external superlattice potential forms a new paradigm for electrostatically tunable excitons in the near- and mid-ultraviolet (UV). The imposed…

A global trend to miniaturization and multiwavelength performance of nanophotonic devices drives research on novel phenomena, such as bound states in the continuum and Mietronics, as well as the survey for high-refractive index and strongly…

We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology. The potential, named GAP-20, describes the properties of the bulk crystalline…

Hexagonal boron nitride (h-BN) is a prominent member in the growing family of two-dimensional materials with potential applications ranging from being an atomically smooth support for other 2D materials to templating growth of molecular…

Hexagonal boron nitride (h-BN) is a promising material for implementation in spintronics due to a large band gap, low spin-orbit coupling, and a small lattice mismatch to graphene and to close-packed surfaces of fcc-Ni(111) and…

We show that the Gaussian Approximation Potential machine learning framework can describe complex magnetic potential energy surfaces, taking ferromagnetic iron as a paradigmatic challenging case. The training database includes total…

Hexagonal boron nitride (h-BN) is a natural hyperbolic material, for which the dielectric constants are the same in the basal plane (epsilon^t = epsilon^x = epsilon^y) but have opposite signs (epsilon^t*epsilon^z < 0) from that in the…