Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization
Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization
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DOI：
10.1103/physrevlett.115.205901
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发表时间：
2015-11-10
影响因子：
8.6
通讯作者：
Tanaka, Isao
中科院分区：
文献类型：
--
作者：
Seko, Atsuto;Togo, Atsushi;Tanaka, Isao
低晶格导热系数（LTC）化合物是寻找具有高转换效率的热电材料所必需的。已经使用了一些策略来降低LTC。然而，这些试验只在有限的勘探空间内取得了成功。在这里，我们报告了一个包含54 779个化合物的文库的虚拟筛选。我们的策略是通过贝叶斯优化来搜索库，使用从一组101个化合物的第一原理非调和晶格动力学计算中获得的LTC初始数据。我们发现了221种LTC非常低的材料。其中两种甚至具有< 1 eV的电子带隙，这使它们成为热电应用的特殊候选者。除了那些新发现的热电材料外，目前的策略被认为对许多其他需要优化材料化学的应用是强有力的。
Compounds of low lattice thermal conductivity (LTC) are essential for seeking thermoelectric materials with high conversion efficiency. Some strategies have been used to decrease LTC. However, such trials have yielded successes only within a limited exploration space. Here, we report the virtual screening of a library containing 54 779 compounds. Our strategy is to search the library through Bayesian optimization using for the initial data the LTC obtained from first-principles anharmonic lattice-dynamics calculations for a set of 101 compounds. We discovered 221 materials with very low LTC. Two of them even have an electronic band gap < 1 eV, which makes them exceptional candidates for thermoelectric applications. In addition to those newly discovered thermoelectric materials, the present strategy is believed to be powerful for many other applications in which the chemistry of materials is required to be optimized.