Cubic halide perovskites as potential low thermal conductivity materials: A combined approach of machine learning and first-principles calculations
Cubic halide perovskites as potential low thermal conductivity materials: A combined approach of machine learning and first-principles calculations
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DOI：
10.1103/physrevb.105.014310
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发表时间：
2022-01
影响因子：
3.7
通讯作者：
Xinming Wang;Yinchang Zhao;Shuming Zeng;Zhuchi Wang;Ying Chen;J. Ni
中科院分区：
文献类型：
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作者：
Xinming Wang;Yinchang Zhao;Shuming Zeng;Zhuchi Wang;Ying Chen;J. Ni
导热系数是影响材料热电性能的关键因素。在这里，机器学习技术和第一性原理计算相结合被用来识别具有超低导热系数的立方卤化物钙钛矿(B=Ca，Cd和Sn.基于弛豫时间近似下的玻尔兹曼输运方程，我们证明了这类钙钛矿在300℃时具有非常低的晶格热导W/mK。我们采用了同时包含三次和四次非谐性的自洽声子理论，该理论是从气泡和环状自能图而不是多体微扰理论来考虑的。结果表明，该方法在温度下产生立方-四方相变，与239K的实验值很好地吻合。在中观察到反常的温度依赖关系，其中相干项占总晶格热导率的26%。我们还证明，低能级声子模中振动的硬化通过减小可用相空间来抵消声子布居效应随温度的升高。
Thermal conductivity is the key factor affecting thermoelectric properties of materials. Here, machine-learning techniques combined with first-principles calculations are used to identify the cubic halide perovskites(B= Ca, Cd, and Sn) with ultralow thermal conductivity. Based on the Boltzmann transport equation within the relaxation time approximation, we demonstrate this type of perovskites have remarkably low lattice thermal conductivitiesW/mK at 300 K. We employ the self-consistent phonon theory incorporating both cubic and quartic anharmonicity, which is considered from the bubble and loop self-energy diagrams rather than many-body perturbation theory. We show that the approach yields a cubic-tetragonal phase transition ofat temperature, in good agreement with the experimental value of 239 K. An anomalously temperature dependence ofis observed in, where the coherent term account for 26% of the total lattice thermal conductivity. We also demonstrate that the hardening of vibrations in low-lying phonon modes offset the phonon population effect as temperature increases by reducing the available phase space.