Low thermal conductivity is important for thermal barrier coatings, thermoelectrics, and other applications in industry and materials science. Accurate calculation of their thermal conductivity $\kappa$ at high temperatures remains challenging: methods such as the Boltzmann transport equation (BTE) usually underestimate the actual value. Here we used the effective harmonic method and homogeneous nonequilibrium molecular dynamics simulations with machine-learning potentials to calculate the thermal conductivity of candidate materials at temperatures up to 1500 K. The results obtained for ${\mathrm{La}}_2{\mathrm{Zr}}_2{\mathrm{O}}_7, {\mathrm{ZrSiO}}_4$, and ${\mathrm{BaZrO}}_3$ are in perfect agreement with the experiment at all temperatures. We used renormalized second- and third-order interatomic force constants and phonons at high temperatures to calculate the thermal conductivity using the BTE and confirmed these results with molecular dynamics simulations. Investigating the relationship of thermal conductivity with the elastic properties, Debye temperature, and the speed of sound, we proposed threshold values for future high-throughput screening for low-$\kappa$ materials. Using the molecular dynamics method at high temperatures, we calculated the volumetric thermal expansion coefficient and selected ten candidate materials for thermal barrier coatings at high temperatures. Besides thermal barrier coating materials, this approach can be applied to multiple classes of materials where thermal conductivity is important.

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寻找用于热障涂层的低导热率材料：理论方法

低导热率对于热障涂层、热电以及工业和材料科学中的其他应用非常重要。准确计算其导热系数$\kappa$在高温下仍然具有挑战性：玻尔兹曼传输方程 (BTE) 等方法通常会低估实际值。在这里，我们使用有效调和方法和具有机器学习潜力的均质非平衡分子动力学模拟来计算候选材料在高达 1500 K 温度下的导热率。获得的结果为${拉}_2{锆}_2{\mathrm{氧}}_7, {\mathrm{氧化锆}}_4$， 和${\mathrm{氧化锆}}_3$与所有温度下的实验完全一致。我们使用重正化的二阶和三阶原子间力常数和高温声子来使用 BTE 计算热导率，并通过分子动力学模拟证实了这些结果。研究热导率与弹性特性、德拜温度和声速的关系，我们提出了未来低通量高通量筛选的阈值。$\kappa$材料。利用高温下分子动力学方法计算了体积热膨胀系数，筛选出10种高温热障涂层候选材料。除了热障涂层材料之外，这种方法还可以应用于导热性很重要的多种材料。