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Complex temperature dependence of coherent and incoherent lattice thermal transport in superlattices

机译:超晶格中相干和非相干晶格热运输的复杂温度依赖性

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摘要

Currently, it is still unclear how and to what extent a change in temperature impacts the relative contributions of coherent and incoherent phonons to thermal transport in superlattices. Some seemingly conflicting computational and experimental observations of the temperature dependence of lattice thermal conductivity make the coherent-incoherent thermal transport behaviors in superlattices even more elusive. In this work, we demonstrate that incoherent phonon contribution to thermal transport in superlattices increases as the temperature increases due to elevated inelastic interfacial transmission. On the other hand, the coherent phonon contribution decreases at higher temperatures due to elevated anharmonic scattering. The competition between these two conflicting mechanisms can lead to different trends of lattice thermal conductivity as temperature increases, i.e. increasing, decreasing, or non-monotonic. Finally, we demonstrate that the neural network-based machine learning model can well capture the coherent-incoherent transition of lattice thermal transport in the superlattice, which can greatly aid the understanding and optimization of thermal transport properties of superlattices.
机译:目前,尚不清楚温度变化如何以及在多大程度上影响相干和非相干声子对超晶格热输运的相对贡献。对于晶格热导率的温度依赖性,一些看似相互矛盾的计算和实验观测使得超晶格中的相干非相干热输运行为变得更加难以捉摸。在这项工作中,我们证明了超晶格中非相干声子对热输运的贡献随着温度的升高而增加,这是由于非弹性界面传输的增加。另一方面,由于非简谐散射的增加,相干声子的贡献在较高温度下降低。这两种相互冲突的机制之间的竞争会导致晶格热导率随温度升高而呈现不同的趋势,即增加、减少或非单调。最后,我们证明了基于神经网络的机器学习模型能够很好地捕捉到超晶格中晶格热输运的相干-非相干转变,这有助于理解和优化超晶格的热输运性质。

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