相关论文: Thermal conductivity of h-BN monolayers using mach…
Fracture toughness ($K_\mathrm{Ic}$) and fracture strength ($\sigma_\mathrm{f}$) are key criteria in the selection and design of reliable ceramics. However, their experimental characterization remains challenging -- especially for ceramic…
This is a theoretical study of electron transport in gated bilayer graphene - a novel semiconducting material with a tunable band gap. It is shown that the which-layer pseudospin coherence enhances the subgap conductivity and facilitates…
The substrate material of monolayer graphene influences the charge carrier mobility by various mechanisms. At room temperature, the scattering of conduction electrons by phonon modes localized at the substrate surface can severely limit the…
Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to {\em ab initio} molecular dynamics (MD) simulations. However, fitting high-quality…
We present measurements of the low temperature thermal conductivity for materials useful in the construction of cryogenic supports for scientific instrumentation and in the fabrication of flat flexible cryogenic cabling. The materials we…
The purpose of this paper is to present a simple micromechanics-based model to estimate the effective thermal conductivity of real-world macroscopically isotropic materials of matrix-inclusion type. The methodology is based on the…
Machine learning interatomic potentials (MLIPs) are used to estimate potential energy surfaces (PES) from ab initio calculations, providing near quantum-level accuracy with reduced computational costs. However, the high cost of assembling…
Chemical and structural diversity present in hexagonal boron nitride ((h-BN) and graphene hybrid nanostructures provide new avenues for tuning various properties for their technological applications. In this paper we investigate the…
The lattice thermal conductivity plays a key role in the performance of thermoelectric materials, where the lower values lead to a higher figure of merit values. Two-dimensional group III-VI monolayers such as InTe are promising materials…
The electrical and thermal behavior of nanoscale devices based on two-dimensional (2D) materials is often limited by their contacts and interfaces. Here we report the temperature-dependent thermal boundary conductance (TBC) of monolayer…
Two-dimensional transition metal dichalcogenides (TMDs) exhibit remarkable thermal anisotropy due to their strong intralayer covalent bonding and weak interlayer van der Waals (vdW) interactions. However, accurately modeling their thermal…
We calculate the phonon thermal conductivity of various moir\'e bilayer systems using a continuum approach and the semiclassical transport theory. When the twist angle is close to 0, we observe a significant reduction of thermal…
We simulate the electronic and transport properties of metal/two-dimensional material/metal vertical heterostructures, with a focus on graphene, hexagonal boron nitride and two phases of molybdenum diselenide. Using density functional…
Access to the potential energy Hessian enables determination of the Gibbs free energy, and certain approaches to transition state search and optimization. Here, we demonstrate that off-the-shelf pretrained Open Catalyst Project (OCP)…
Machine Learning Interatomic Potentials (MLIP) are a novel in silico approach for molecular property prediction, creating an alternative to disrupt the accuracy/speed trade-off of empirical force fields and density functional theory (DFT).…
Foundational machine learning interatomic potentials (MLIPs) are being developed at a rapid pace, promising closer and closer approximation to ab initio accuracy. This unlocks the possibility to simulate much larger length and time scales.…
Machine learning interatomic potentials (MLIPs) enables molecular dynamics (MD) simulations with ab initio accuracy and has been applied to various fields of physical science. However, the performance and transferability of MLIPs are…
Understanding the limits of phononic heat dissipation from a two-dimensional layered material (2DLM) to its hexagonal boron nitride (h-BN) substrate and how it varies with the structure of the 2DLM is important for the design and thermal…
Lattice thermal conductivity (kL) is a crucial physical property of crystals with applications in thermal management, such as heat dissipation, insulation, and thermoelectric energy conversion. However, accurately and rapidly determining kL…
Precise modeling and understanding of heat transport in the superionic phase are of great interest. Although simulations combining Green-Kubo (GK) molecular dynamics with machine-learned potentials (MLPs) stand as a promising approach,…