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

The importance of many-body dispersion effects in layered materials subjected to high external loads is evaluated. State-of-the-art many-body dispersion density functional theory calculations performed for graphite, hexagonal boron nitride,…

We demonstrate that freeing a single-atom thick layer of hexagonal boron nitride (hbn) from tight chemical bonding to a Ni(111) thin film grown on a W(110) substrate can be achieved by intercalation of Au atoms into the interface. This…

Hexagonal boron nitride (h-BN) encapsulation significantly improves carrier transport in graphene. This work investigates the benefit of implementing the encapsulation technique in graphene field-effect transistors (GFET) in terms of their…

Hexagonal boron nitride ($\it h$-BN) exhibits dominant $\pi$-bands near the Fermi level, similar to graphene. However, unlike graphene, where tight-binding (TB) models accurately reproduce band edges near the $K$ and $K^{\prime}$ points in…

We combine electron energy-loss spectroscopy and first-principles calculations based on density-functional theory (DFT) to identify the lowest indirect exciton state in the in-plane charge response of hexagonal boron nitride (h-BN) single…

Two dimensional materials are an emerging class of materials which is transforming the present day research activity on a phenomenal scale. Hexagonal boron nitride is a wide band gap 2D material which is an excellent substrate for graphene…

Topological physics has evolved from its initial focus on fermionic systems to the exploration of bosonic systems, particularly phononic excitations in crystalline materials. Two-dimensional (2D) topological phonons emerge as promising…

Recent advancements in thermal conductivity modulating strategies have shown promising enhancements to the thermal management capabilities of two-dimensional materials. In this article, both iterative Boltzmann transport equation solution…

Graphene is one of the most researched two dimensional (2D) material due to its unique combination of mechanical, thermal and electrical properties. Special 2D structure of graphene enables it to exhibit a wide range of peculiar material…

Hexagonal boron nitride (hBN) and graphene are similar in many ways - they are isoelectronic, have the same structure, are chemically inert and show persistence. All of these properties are indicators of a deeper connection that has, thus…

The thermoelectric properties of hybrid graphene-boron nitride nanoribbons (BCNNRs) are investigated using the non-equilibrium Green's function (NEGF) approach. We find that the thermoelectric figure of merit (ZT) can be remarkably enhanced…

Phonon polaritons are quasiparticles resulting from strong coupling of photons with optical phonons. Excitation and control of these quasiparticles in 2D materials offer the opportunity to confine and transport light at the nanoscale. Here,…

This study introduces an anisotropic interfacial potential that provides an accurate description of the van der Waals (vdW) interactions between water and hexagonal boron nitride (h-BN) at their interface. Benchmarked against the strongly…

Machine Learning (ML) potentials such as Gaussian Approximation Potential (GAP) have demonstrated impressive capabilities in mapping structure to properties across diverse systems. Here, we introduce a GAP model for low-dimensional Ni…

Stacking-dependent interlayer interactions are important for understanding the structural and electronic properties in incommensurable two dimensional material assemblies where long-range moir\'e patterns arise due to small lattice constant…

We demonstrate snake-like motion of graphene nanoribbons atop graphene and hexagonal boron nitride (h-BN) substrates using fully-atomistic non-equilibrium molecular dynamics simulations. The sliding dynamics of the edge-pulled nanoribbons…

Machine learning (ML) has become widely used in the development of interatomic potentials for molecular dynamics simulations. However, most ML potentials are still much slower than classical interatomic potentials and are usually trained…

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…

Hexagonal boron nitride (hBN) regains interest as a strategic component in graphene engineering and in van der Waals heterostructures built with two dimensional materials. It is crucial then, to handle reliable characterization techniques…

Vertical integration of two-dimensional materials has recently emerged as an exciting method for the design of novel electronic and optoelectronic devices. Using density functional theory, we investigatethe structural and electronic…