Related papers: Machine Learning Potential for Hexagonal Boron Nit…

Hexagonal boron nitride (hBN) and black phosphorus (bP) are crystalline materials that can be seen as ordered stackings of two-dimensional layers, which lead to outstanding anisotropic physical properties. The knowledge of the thermal…

In 2015, a new two dimensional (2D) carbon allotrope, called phagraphene, was theoretically proposed. Based on this structure, we propose here a new boron nitride structure called phaBN. It is composed by three types of rings: pentagons,…

Hydrogen is an important building block in global strategies towards a future green energy system. To make this transition possible, intense scientific efforts are needed, also in the field of materials science. Two-dimensional crystals,…

We introduce a new class of machine learning interatomic potentials - fast General Two- and Three-body Potential (GTTP), which is as fast as conventional empirical potentials and require computational time that remains constant with…

Comparative molecular dynamics simulations of a hexamer cluster of the protic ionic liquid ethylammonium nitrate are performed using density functional theory (DFT) and density functional-based tight binding (DFTB) methods. The focus is on…

As a two-dimensional (2D) dielectric material, hexagonal boron nitride (hBN) is in high demand for applications in photonics, nonlinear optics, and nanoelectronics. Unfortunately, the high-throughput preparation of macroscopic-scale,…

Optical imaging beyond the diffraction limit was one of the primary motivations for negative-index metamaterials, resulting in Pendry's perfect lens and the more attainable superlens. While these approaches offer sub-diffractional…

Group theory and density functional theory methods are combined to obtain compact and accurate $k\cdot p$ Hamiltonians that describe the bandstructures around the $K$ and $\Gamma$ points for the 2D material hexagonal boron arsenide (h-BAs)…

Hydrogenation of amorphous silicon (a-Si:H) is critical for reducing defect densities, passivating mid-gap states and surfaces, and improving photoconductivity in silicon-based electro-optical devices. Modelling the atomic scale structure…

There is a large discrepancy between the experimental observations and the theoretical predictions in the morphology of hexagonal boron nitride (h-BN) nanosheets. Theoretically-predicted hexagons terminated by armchair edges are not…

Using the Bardeen tunneling method with first-principles wave functions, computations are made of the tunneling current in graphene / hexagonal-boron-nitride / graphene (G/h-BN/G) vertical structures. Detailed comparison with prior…

We combine density-functional tight-binding (DFTB) with deep tensor neural networks (DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and vibrational molecular properties. The DTNN is used to learn a…

In this work, we present a machine-learned interatomic potential for the ${\alpha}$-Fe-H system based on the tabulated Gaussian Approximation Potential (tabGAP) formalism. Trained on a Density Functional Theory (DFT) dataset of atomic…

Van der Waals heterostructures are at the forefront in materials heterostructure engineering, offering the ultimate control in layer selectivity and capability to combine virtually any material. Hexagonal boron nitride (hBN), the most…

Hexagonal boron nitride (h-BN) is deposited on Si <100> wafer ($\approx$20 cm2) via Plasma Enhanced Chemical Vapor Deposition (PECVD) using a ns-pulsed N2/Ar Micro Hollow Cathode Discharge (MHCD) as a microplasma source. For the first time,…

Dielectrics are insulating materials used in many different electronic devices and play an important role in all of them. Current advanced electronic devices use dielectric materials with a high dielectric constant and avoid high leakage…

High-efficient heat dissipation plays critical role for high-power-density electronics. Experimental synthesis of ultrahigh thermal conductivity boron arsenide (BAs, 1300 W m-1K-1) cooling substrates into the wide-bandgap semiconductor of…

Novel uses for 2-dimensional materials like graphene and hexagonal boron nitride (h-BN) are being frequently discovered especially for membrane and catalysis applications. Still however, a great deal remains to be understood about the…

Hexagonal boron nitride (hBN), having an in-plane hexagonal structure in the sp2 arrangement of atoms, proclaims structural similarity with graphene with only a small lattice mismatch. Despite having nearly identical atomic arrangements and…

The corrosion of metallic surfaces poses significant challenges across industries such as petroleum, energy, and biomedical sectors, leading to structural degradation, safety risks, and substantial maintenance costs. Traditional organic and…