Although anisotropic phononic crystals (PnCs) could be utilized to control the phonon dispersions and thermal transports, rapidly discovering their properties presents a significant challenge due to the enormous consumption of traditional computational methods. In this study, we have developed machine learning techniques to forecast the thermal conductance of anisotropic PnCs (GPnC and GPnC/Gmem) based on the elastic constants, taking conventional inorganic and halide perovskites as examples for their thermoelectric applications. Our findings suggest that predicting GPnC/Gmem is more challenging than predicting GPnC attribute to the complex influence factors and spatial distribution patterns of the former. The GPnC and GPnC/Gmem of the weakest thermal anisotropic materials—all hexagonals are invariants in the (0 0 1) plane, because the velocities in this plane are direction-independent. The GPnC and GPnC/Gmem of the strongest thermal anisotropic material FAPbI3 reaches the minimum and maximum values in [1 1 0] and [1 0 0] directions, respectively. Ultimately, our machine learning models can map the hidden complex nonlinear relationships between target thermal properties and mechanical features to provide valuable insight for accurate and efficient prediction and analysis of the thermal behaviors of PnCs at a mesoscopic level under low temperatures.

中文翻译：

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通过机器学习预测声子晶体中的热传输特性

尽管各向异性声子晶体（PnC）可用于控制声子色散和热传输，但由于传统计算方法的巨大消耗，快速发现其特性提出了重大挑战。在这项研究中，我们开发了机器学习技术，以传统无机和卤化物钙钛矿作为热电应用的例子，根据弹性常数预测各向异性PnC（GPnC和GPnC/Gmem）的热导率。我们的研究结果表明，预测 GPnC/Gmem 比预测 GPnC 更具挑战性，因为前者具有复杂的影响因素和空间分布模式。最弱热各向异性材料的 GPnC 和 GPnC/Gmem — 所有六边形在 (0 0 1) 平面中都是不变量，因为该平面中的速度与方向无关。最强热各向异性材料FAPbI3的GPnC和GPnC/Gmem分别在[1 1 0]和[1 0 0]方向达到最小值和最大值。最终，我们的机器学习模型可以映射目标热性能和机械特征之间隐藏的复杂非线性关系，为在低温下介观水平上准确有效地预测和分析 PnC 的热行为提供有价值的见解。