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.

翻译：基于原子团簇展开（ACE）框架，我们开发了一种机器学习原子间势函数，用于快速精确模拟纤锌矿氮化铝的声子输运特性。通过对比密度泛函理论（DFT）计算，我们验证了该ACE势函数在预测w-AlN多种物性方面的能力，包括基态晶格参数、比热容、热膨胀系数、体积模量以及谐波声子色散关系。通过将ACE预测值与DFT计算及实验结果进行对比，进一步验证了晶格热导率的准确性，充分展现了该ACE势函数在准确描述非谐声子相互作用方面的整体性能。作为实际应用，我们利用该势函数进行晶格动力学分析，揭示了双轴应变对w-AlN热导率及声子特性的影响规律，并确认该应变是影响w-AlN基电子器件近结热设计的关键调控因素。