相关论文: Thermal conductivity of h-BN monolayers using mach…

Machine-learned interatomic potentials (MLIPs) have become the gold standard for atomistic simulations, yet their extension to magnetic materials remains challenging because spin fluctuations must be captured either explicitly or…

The two-dimensional atomically thin insulator hexagonal boron nitride (h-BN) constitutes a new paradigm in tunnel based devices. A large band gap along with its atomically flat nature without dangling bonds or interface trap states makes it…

We study the interfacial thermal conductance of grain boundaries (GBs) between monolayer graphene and hexagonal boron nitride (h-BN) sheets using a combined atomistic approach. First, realistic samples containing graphene/h-BN GBs with…

We present a machine-learning interatomic potential (MLIP) framework, which substantially accelerates the prediction of lattice thermal conductivity for both particle-like and wave-like thermal transport, including three-phonon and…

Quantitative descriptions of the structure-thermal property correlation have been a bottleneck in designing materials with superb thermal properties. In the past decade, the first-principles phonon calculations using density functional…

Machine-learning interatomic potentials (MLIPs) have become a mainstay in computationally-guided materials science, surpassing traditional force fields due to their flexible functional form and superior accuracy in reproducing physical…

By using a first-principles approach, monolayer PbI$_2$ is found to have great potential in thermoelectric applications. The linear Boltzmann transport equation is applied to obtain the perturbation to the electron distribution by different…

Engineering materials with high thermal conductivity are of fundamental interest for efficiently dissipating heat in micro/nanoelectronics. Using first principles computations we report an ultra-high thermal conductivity of 2090 Wm-1K-1…

Anisotropic nanomaterials possess interesting thermal transport properties because they allow orientation of heat fluxes along preferential directions due to a high ratio (up to three orders of magnitude) between their in-plane and…

Thermal conductivity of homogeneous twisted stacks of graphite is found to strongly depend on the misfit angle. The underlying mechanism relies on the angle dependence of phonon-phonon couplings across the twisted interface. Excellent…

The acceleration of material property calculations while maintaining ab initio accuracy (1 meV/atom) is one of the major challenges in computational physics. In this paper, we introduce a Python package enhancing the computation of (finite…

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)…

Accurately modeling the structural reconstruction and thermodynamic behavior of van der Waals (vdW) heterostructures remains a significant challenge due to the limitations of conventional force fields in capturing their complex mechanical,…

The investigation of thermal properties of recently emerged two-dimensional (2D) materials is a necessary step towards fulfilling their potential applications in nano-electronics devices. In this study, the thermal conductivity of novel…

The subject of this paper is the technology (the "how") of constructing machine-learning interatomic potentials, rather than science (the "what" and "why") of atomistic simulations using machine-learning potentials. Namely, we illustrate…

Machine learning interatomic potentials (MLIPs) offer first-principles accuracy with reduced computational cost, but their transferability across different thermodynamic states remains questionable, particularly for fluid systems where…

Uranium monocarbide (UC) is an advanced ceramic fuel candidate due to its superior uranium density and thermal conductivity compared to traditional fuels. To accurately model UC at reactor operating conditions, we developed a machine…

We use a phase field crystal model to generate large-scale bicrystalline and polycrystalline single-layer hexagonal boron nitride (h-BN) samples and employ molecular dynamics (MD) simulations with the Tersoff many-body potential to study…

In recent years, machine learning interatomic potentials (MLIPs) have attracted significant attention as a method that enables large-scale, long-time atomistic simulations while maintaining accuracy comparable to electronic structure…

Tuning thermal transport in nanostructured materials is a powerful approach to develop high-efficiency thermoelectric materials. Using a recently developed approach based on the phonon mean free path dependent Boltzmann transport equation,…