MoS phononic crystals for advanced thermal management.

Effective thermal management of electronic devices encounters substantial challenges owing to the notable power densities involved. Here, we propose layered MoS phononic crystals (PnCs) that can effectively reduce thermal conductivity (κ) with relatively small disruption of electrical conductivity (σ), offering a potential thermal management solution for nanoelectronics. These layered PnCs exhibit remarkable efficiency in reducing κ, surpassing that of Si and SiC PnCs with similar periodicity by ~100-fold.

Multiscale machine-learning interatomic potentials for ferromagnetic and liquid iron.

A large and increasing number of different types of interatomic potentials exist, either based on parametrised analytical functions or machine learning. The choice of potential to be used in a molecular dynamics simulation should be based on the affordable computational cost and required accuracy. We develop and compare four interatomic potentials of different complexity for iron: a simple machine-learned embedded atom method (EAM) potential, a potential with machine-learned two- and three-body-dependent terms, a potential with machine-learned EAM and three-body terms, and a Gaussian approximation potential with the smooth overlap of atomic positions descriptor.

Machine-learning interatomic potential for W-Mo alloys.

In this work, we develop a machine-learning interatomic potential for WMorandom alloys. The potential is trained using the Gaussian approximation potential framework and density functional theory data produced by the Viennasimulation package. The potential focuses on properties such as elastic properties, melting, and point defects for the whole range of WMocompositions.