Using the atomic cluster expansion (ACE) framework, we develop a machine learning interatomic potential for fast and accurately modelling the phonon transport properties of wurtzite aluminum nitride. 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.

使用原子团簇展开（ACE）框架，不断发展出一种适用于快速准确建模五硝基金刚酸铝的声子传输特性的机器学习原子间势。通过对w-AlN的一系列性质，包括基态晶格参数、比热容、热膨胀系数、体模量和谐振声子色散等进行ACE势对密度泛函理论（DFT）的预测能力展示。通过将ACE预测值与DFT计算和实验进行比较，验证晶格热导率，并展示我们的ACE势在描述非谐振声子相互作用方面的整体能力。作为实际应用，我们使用该势进行晶格动力学分析，揭示双轴应变对w-AlN的热导率和声子特性的影响，这被确定为调节w-AlN基电子器件近接热设计的重要因素。

基于机器学习的快速和量子精确的热模拟石英型AlN的原子簇展开势能