Machine-learned atomic cluster expansion potentials for fast and quantum-accurate thermal simulations of wurtzite AlN
Guang Yang, Yuanbin Liu, Lei Yang, Bing Cao
Abstract
Thermal transport in wurtzite aluminum nitride (w-AlN) significantly affects the performance and reliability of corresponding electronic devices, particularly when lattice strains inevitably impact the thermal properties of w-AlN in practical applications. To accurately model the thermal properties of w-AlN with high efficiency, we develop a machine learning interatomic potential based on the atomic cluster expansion (ACE) framework. The predictive power of the ACE potential against density functional theory (DFT) is demonstrated across a broad range of properties of w-AlN, including ground-state lattice parameters, specific heat capacity, coefficients of thermal expansion, bulk modulus, and harmonic phonon dispersions. Validation of lattice thermal conductivity is further carried out by comparing the ACE-predicted values to the DFT calculations and experiments, exhibiting the overall capability of our ACE potential in sufficiently describing anharmonic phonon interactions. As a practical application, we perform a lattice dynamics analysis using the potential to unravel the effects of biaxial strains on thermal conductivity and phonon properties of w-AlN, which is identified as a significant tuning factor for near-junction thermal design of w-AlN-based electronics.