关键词: boron dichalcogenides, thermal conductivity, machine learning interatomic potentials, first-principles calculation

Abstract: The investigation of thermal transport is crucial to the thermal management of modern electronic devices. To obtain the thermal conductivity through solution of the Boltzmann transport equation, calculation of the anharmonic interatomic force constants has a high computational cost based on the current method of single-point density functional theory force calculation. The recent suggested machine learning interatomic potentials (MLIPs) method can avoid these huge computational demands. In this work, we study the thermal conductivity of two-dimensional MoS$_{2}$-like hexagonal boron dichalcogenides (H-B$_{2}{VI}_{2}$; ${VI} = {\rm S}$, Se, Te) with a combination of MLIPs and the phonon Boltzmann transport equation. The room-temperature thermal conductivity of H-B$_{2}$S$_{2}$ can reach up to 336 W$\cdot $m$^{-1}\cdot $K$^{-1}$, obviously larger than that of H-B$_{2}$Se$_{2}$ and H-B$_{2}$Te$_{2}$. This is mainly due to the difference in phonon group velocity. By substituting the different chalcogen elements in the second sublayer, H-B$_{2}{VI}{VI}^\prime $ have lower thermal conductivity than H-B$_{2}{VI}_{2}$. The room-temperature thermal conductivity of B$_{2}$STe is only 11% of that of H-B$_{2}$S$_{2}$. This can be explained by comparing phonon group velocity and phonon relaxation time. The MLIP method is proved to be an efficient method for studying the thermal conductivity of materials, and H-B$_{2}$S$_{2}$-based nanodevices have excellent thermal conduction.

Key words: boron dichalcogenides, thermal conductivity, machine learning interatomic potentials, first-principles calculation