Structure and lattice thermal conductivity of grain boundaries in silicon by using machine learning potential and molecular dynamics
Structure and lattice thermal conductivity of grain boundaries in silicon by using machine learning potential and molecular dynamics
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DOI：
10.1016/j.commatsci.2021.111137
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发表时间：
2021-05
影响因子：
3.3
通讯作者：
S. Fujii;Atsuto Seko
中科院分区：
文献类型：
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作者：
S. Fujii;Atsuto Seko
在硅中，晶格热导率在热电和微电子器件等广泛应用中起着重要作用。多晶硅的晶界可以显著降低晶格导热系数，但晶界原子结构对晶格导热系数的影响尚不清楚。本研究展示了利用机器学习电位（MLPs）对硅中GB结构、GB能量和GB声子性质的准确预测。结果表明，由于mlp是由涵盖各种结构的训练数据集开发的，因此mlp能够实现鲁棒的GB结构搜索。我们还利用大尺度扰动分子动力学和声子波包模拟研究了四GB原子结构中的晶格热传导。这些结果的比较表明，热导率的GB结构依赖源于GB的非谐波振动，而不是源于GB的声子传输行为。与典型硅的经验电位相比，mlp的优势也得到了深入的研究。
In silicon, lattice thermal conductivity plays an important role in a wide range of applications such as thermoelectric and microelectronic devices. Grain boundaries (GBs) in polycrystalline silicon can significantly reduce lattice thermal conductivity, but the impact of GB atomic structures on it remains to be elucidated. This study demonstrates accurate predictions of the GB structures, GB energies, and GB phonon properties in silicon using machine learning potentials (MLPs). The results indicate that the MLPs enable robust GB structure searches owing to the fact that the MLPs were developed from a training dataset covering a wide variety of structures. We also investigate lattice thermal conduction at four GB atomic structures using large-scale perturbed molecular dynamics and phonon wave-packet simulations. The comparison of these results indicates that the GB structure dependence of thermal conductivity stems from anharmonic vibrations at GBs rather than from the phonon transmission behavior at GBs. The advantages of the MLPs compared with a typical empirical potential of silicon are also thoroughly investigated.