npj Computational Materials: 武汉大学刘惠军教授: 胸有合金构型熵,预测热导率不慌

随着各式各样的电子器件的广泛应用，材料的热导率开始扮演起越来越重要的角色。具有较高热导率的材料可以实现有效散热，较低的则适合用做隔热或热电材料，因此，准确计算体系的晶格热导率具有十分重要的意义，也是目前科学研究的焦点之一。然而，通常采用的第一性原理或分子动力学模拟需要消耗大量的计算资源或构建复杂的原子间势函数，这些问题对于具有分数化学计量比的合金体系来说则更为突出。

来自武汉大学物理科学与技术学院的刘惠军教授团队以辉碲铋矿族化合物为例，基于构型熵的概念实现了对具有任意化学配比体系晶格热导率的快速准确预测。该方法无需耗时的第一性原理计算，仅仅用到了少量具有整数配比体系的晶格热导率。研究发现，该方法给出的预测结果与实验结果非常符合，平均绝对误差仅为0.04 W/mK，表明其强大的预测能力。此外，该方法在预测半赫斯勒化合物晶格热导率时也同样表现优异，表明这种基于构型熵的方法实际上可以适用于任何材料家族。这项研究工作对于高通量筛选具有特定晶格热导率的体系具有重要的借鉴作用。

该文近期发表于npj Computational Materials 8,: 75 (2022) ，英文标题与摘要如下。

Predicting the lattice thermal conductivity of alloyed compounds from the perspective of configurational entropy 

Mengke Li, Guohua Cao, Yufeng Luo, Caiyu Sheng & Huijun Liu

Accurate evaluation of lattice thermal conductivity is usually a tough task from the theoretical side, especially for alloyed systems with fractional stoichiometry. Using the tetradymite family as a prototypical class of examples, we propose a reliable approach for rapid prediction on the lattice thermal conductivity at arbitrary composition by utilizing the concept of configurational entropy. Instead of performing time-consuming first-principles calculations, the lattice thermal conductivities of any alloyed tetradymites can be readily obtained from a few samples with integer stoichiometry. The strong predictive power is demonstrated by good agreement between our results and those reported experimentally. In principle, such an effective method can be applicable to any other material families, which is very beneficial for high-throughput design of systems with desired thermal conductivity.

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