Thermal management materials are of critical importance for engineering miniaturized electronic devices, where theoretical design of such materials demands numerically expensive calculations. In this work, we applied the recently developed machine learning interatomic potential (MLIP) to evaluate the thermal conductivity of hexagonal boron nitride monolayers. The MLIP is obtained using the Gaussian approximation potential (GAP) method, and the resulting lattice dynamical properties and thermal conductivity are compared with those obtained from explicit frozen phonon calculations. It is observed that accurate thermal conductivity can be obtained based on MLIP constructed with 30% representative configurations, and the high-order force constants provide a more reliable benchmark on the quality of MLIP than the harmonic approximation.

中文翻译：

---

使用机器学习原子间势的 h-BN 单层的热导率

热管理材料对于工程小型化电子设备至关重要，其中此类材料的理论设计需要进行昂贵的数值计算。在这项工作中，我们应用最近开发的机器学习原子间势 (MLIP) 来评估六方氮化硼单层的热导率。MLIP 是使用高斯近似势 (GAP) 方法获得的，并将所得的晶格动力学性质和热导率与从显式冻结声子计算中获得的值进行比较。可以观察到，基于 30% 代表性配置构建的 MLIP 可以获得准确的热导率，并且高阶力常数为 MLIP 的质量提供了比谐波近似更可靠的基准。