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.

中文翻译：

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超晶格中相干和非相干晶格热传输的复杂温度依赖性

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传输的相对贡献。对晶格热导率的温度依赖性的一些看似矛盾的计算和实验观察使得超晶格中的相干-非相干热传输行为更加难以捉摸。在这项工作中，我们证明了非相干声子对超晶格中热传输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，由于非谐散射升高，相干声子贡献在较高温度下降低。这两种相互矛盾的机制之间的竞争会导致晶格热导率随着温度的升高而呈现不同的趋势，即升高、降低或非单调。最后，我们证明了基于神经网络的机器学习模型可以很好地捕捉超晶格中晶格热输运的相干-非相干转变，这可以极大地帮助理解和优化超晶格的热输运性质。