Abstract:

Due to the hexagonal structure, thermal stability, and wide bandgap, two-dimensional group-Ⅲ nitrides (h-BN, h-AlN, h-GaN and h-InN) show great promise for electronic and optoelectronic applications. Density functional theory (DFT) and classical molecular dynamics (MD) methods have advantages in calculation accuracy and scale respectively, but they are limited in the application of high-precision large-scale structure and performance research. Herein, we employ deep potential (DP) method to construct a high-precision machine learning potential (MLP) and systematically investigate the lattice dynamics, thermodynamic, mechanical, and thermal transport properties of two-dimensional Group Ⅲ nitrides. The DP method can achieve DFT accuracy in energy and atomic force predictions and accurately reproduce phonon dispersion and thermodynamic functions (free energy, heat capacity, entropy) across the 0–1200 K temperature range. MD simulations of uniaxial tensiles reveal distinct mechanical behavior differences among materials. h-BN exhibits high strength but brittle fracture characteristics, while h-AlN and h-GaN demonstrate good strength and ductility. h-InN shows relatively weak overall mechanical performance. Non-equilibrium MD simulations on thermal conductivity reveal significant length-dependent effects in h-BN and h-AlN, attributed to longer phonon mean free paths. Enhanced phonon scattering in h-GaN and h-InN results in lower thermal conductivities. These findings demonstrate that the DP method combines DFT accuracy with large-scale simulation capabilities can deepen understanding of structures and properties of two-dimensional Group Ⅲ nitrides and provide a computational framework and theoretical foundations for material design and device application.

Key words: Group-Ⅲ nitride, Machine learning potential, Deep potential, Molecular dynamics, Thermal conductivity