Related papers: Machine Learning Potential for Hexagonal Boron Nit…

We present a novel method to establish inner point contacts on hexagonal boron nitride (hBN) encapsulated graphene heterostructures with dimensions as small as 100 nm by pre-patterning the top-hBN in a separate step prior to dry-stacking. 2…

The interaction between graphene and hexagonal boron nitride (hBN) plays a pivotal role in determining the electronic and structural properties of graphene-based devices. In this work, we employ quantum Monte Carlo (QMC) to study the…

Periodically corrugated epitaxial graphene and hexagonal boron nitride (h-BN) on metallic substrates are considered as perspective templates for the self-assembly of nanoparticles arrays. By using first-principles calculations, we determine…

Interconnect materials with ultralow dielectric constant, and good thermal and mechanical properties are crucial for the further miniaturization of electronic devices. Recently, it has been demonstrated that ultrathin amorphous boron…

Nonlinear light-matter interactions in structured materials are the source of exciting properties and enable vanguard applications in photonics. However, the magnitude of nonlinear effects is generally small, thus requiring high optical…

We demonstrate how machine-learning based interatomic potentials can be used to model guest atoms in host structures. Specifically, we generate Gaussian approximation potential (GAP) models for the interaction of lithium atoms with…

Hexagonal boron nitride (h-BN) is unique among two-dimensional materials, with a large band gap (~6 eV) and high thermal conductivity (>400 W/m/K), second only to diamond among electrical insulators. Most electronic studies to date have…

Light properties in the mid-infrared can be controlled at a deep subwavelength scale using hyperbolic phonons-polaritons (HPPs) of hexagonal boron nitride (h-BN). While propagating as waveguided modes HPPs can concentrate the electric field…

This study investigates the thermal expansion coefficient of two-dimensional (2D) functionalized boron nitride (f-BN) materials using first-principles density functional theory (DFT). Two-dimensional materials, particularly hexagonal boron…

Moire superlattices (MSLs) are an emerging class of two-dimensional functional materials whose electronic states can be tuned by the twist angle between two van der Waals layers and/or the relative placement of the layers. The intriguing…

The development of machine learning interatomic potentials has immensely contributed to the accuracy of simulations of molecules and crystals. However, creating interatomic potentials for magnetic systems that account for both magnetic…

Lateral heterostructures of two-dimensional materials may exhibit various intriguing emergent properties. Yet when specified to the orientationally aligned heterojunctions of zigzag graphene and hexagonal boron nitride (hBN) nanoribbons,…

Hexagonal boron nitride (h-BN) is well-established as a requisite support, encapsulant and barrier for 2D material technologies, but also recently as an active material for applications ranging from hyperbolic metasurfaces to room…

The production of high-quality hexagonal boron nitride (h-BN) is essential for the ultimate performance of two-dimensional (2D) materials-based devices, since it is the key 2D encapsulation material. Here, a decisive guideline is reported…

Hexagonal boron nitride (hBN) is a wide band gap material with both strong excitonic light emission in the ultraviolet and strong exciton-phonon coupling. Luminescence experiments performed on the recently synthesized monolayer form (m-hBN)…

Among two-dimensional atomic crystals, hexagonal boron nitride (hBN) is one of the most remarkable materials to fabricate heterostructures revealing unusual properties. We perform first-principles calculations to determine whether…

Machine learning potentials have emerged as a powerful tool to extend the time and length scales of first principles-quality simulations. Still, most machine learning potentials cannot distinguish different electronic spin orientations and…

We report a theoretical study of dielectric properties of models of amorphous Boron Nitride, using interatomic potentials generated by machine learning. We first perform first-principles simulations on small (about $100$ atoms in the…

Uranium mononitride (UN) is a promising accident-tolerant fuel because of its high fissile density and high thermal conductivity. In this study, we developed the first machine learning interatomic potentials for reliable atomic-scale…

The integrated inplane growth of two dimensional materials with similar lattices, but distinct electrical properties, could provide a promising route to achieve integrated circuitry of atomic thickness. However, fabrication of edge specific…