High Thermal Conductivity of Wurtzite Boron Arsenide Predicted by Including Four-Phonon Scattering with Machine Learning Potential

Materials with high thermal conductivity are of great importance to the thermal management of modern electronic devices. Recently, it was found that cubic boron arsenide (c-BAs) is a high thermal conductivity (κ) material with a value of ∼1300 W/(m·K) at room temperature (RT), where four-phonon scattering plays a crucial role in limiting the κ. In this work, with four-phonon scattering included, we find that the κ of wurtzite BAs (w-BAs) reaches as high as 1036 W/(m·K) along the a–b plane at RT, decreasing by 43% compared to the calculation without considering four-phonon scattering. The similar phonon transport properties between c-BAs and w-BAs can be understood in terms of similar projected density of states and scattering rates, which have the origin in crystal structural resemblance. To accelerate the calculation, the moment tensor potential derived from machine learning is adopted and proven to be a reliable and efficient method to obtain high-order interatomic force constants.高导热材料对于现代电子器件的热管理具有至关重要的意义。近期研究发现，立方砷化硼（cubic boron arsenide, c-BAs）是一种高导热系数（thermal conductivity, κ）材料，室温（room temperature, RT）下其导热系数约为1300 W/(m·K)，其中四声子散射对其导热系数的限制起到关键作用。本研究在考虑四声子散射的前提下，发现纤锌矿型砷化硼（wurtzite BAs, w-BAs）在室温下沿a-b平面的导热系数可达1036 W/(m·K)，相较于未考虑四声子散射的计算结果降低了43%。立方砷化硼与纤锌矿型砷化硼之间相似的声子输运特性，可以通过二者相似的投影态密度（projected density of states）与散射率得到合理解释，这一特性源于两者晶体结构的相近性。为加速计算流程，本研究采用了基于机器学习（machine learning）得到的矩张量势，并证实该方法是获取高阶原子间力常数的可靠且高效的技术手段。