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首页> 外文期刊>Journal of Physics, D. Applied Physics: A Europhysics Journal >A high-throughput descriptor for prediction of lattice thermal conductivity of half-Heusler compounds
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A high-throughput descriptor for prediction of lattice thermal conductivity of half-Heusler compounds

机译:一种高吞吐量描述符,用于预测半起式化合物的晶格导热率

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摘要

Lattice thermal conductivity is a key property of materials for applications ranging from thermal barrier coatings to thermoelectric conversion. In principle, the thermal conductivity can be accurately predicted by solving the phonon Boltzmann transport equation, which is however very time-consuming, especially for complex systems with large unit cell. Here, using half-Heusler compounds as prototypical examples, we apply a compressed-sensing approach to rapidly evaluate the lattice thermal conductivity with very good accuracy, as realized by a physically interpretable descriptor. Beyond the initial 86 training data, the descriptor is employed to predict the thermal conductivities of 75 half- and 15 full-Heusler compounds, which shows good agreement with explicit first-principles results. Moreover, the descriptor is further optimized by including only the fundamental properties of the constituent atoms, which could accelerate materials discovery with desirable thermal conductivity.
机译:晶格导热率是用于从热阻挡涂层到热电转换的应用的材料的关键特性。 原则上,通过求解声子Boltzmann传送方程,可以准确地预测导热率,这对于具有大单元电池的复杂系统而言,这是非常耗时的。 这里,使用半发生素化合物作为型原型示例,我们应用一种压缩感测方法,以通过物理可解释的描述符实现的非常好的精度来快速评估晶格导热率。 除了初始的86训练数据之外,使用描述符来预测75个半和15个全Heusler化合物的导热性,这表现出与明确的第一原理结果良好的一致性。 此外,通过仅包括组成原子的基本特性,进一步优化描述符,其可以通过所需的导热率加速材料发现。

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