论文标题
H-BN单层的导热率使用机器学习间势
Thermal conductivity of h-BN monolayers using machine learning interatomic potential
论文作者
论文摘要
热管理材料对于工程微型化的电子设备至关重要，其中此类材料的理论设计需要评估数值昂贵的导热率。在这项工作中，我们应用了最近开发的机器学习跨性能（MLIP）来评估六角形硝酸硼单层的导热率。使用高斯近似势（GAP）方法获得MLIP，并将所得的晶格动力学和导热率与从显式冷冻的声子计算获得的晶格动力学和导热率进行了比较。可以观察到，可以根据大约30％的代表性配置构建的MLIP来获得准确的热导率，并且高阶力常数比谐波近似相比，MLIP的质量为MLIP的质量提供了更可靠的基准。
Thermal management materials are of critical importance for engineering miniaturized electronic devices, where theoretical design of such materials demands the evaluation of thermal conductivities which are numerically expensive. 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 about 30% representative configurations, and the high-order force constants provide a more reliable benchmark on the quality of MLIP than the harmonic approximation.
