4.8 Article

High-Throughput Screening of Rattling-Induced Ultralow Lattice Thermal Conductivity in Semiconductors

Journal

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
Volume 144, Issue 10, Pages 4448-4456

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/jacs.1c11887

Keywords

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Funding

  1. National Natural Science Foundation of China [22173093, 21688102]
  2. Hefei National Laboratory for Physical Sciences at the Microscale [KF2020003, SK2340002001]
  3. Chinese Academy of Sciences Pioneer Hundred Talents Program [KJ2340007002]
  4. National Key Research and Development Program of China [2016YFA0200604]
  5. Anhui Initiative in Quantum Information Technologies [AHY090400]
  6. Center of Chinese Academy Project for Young Scientists in Basic Research [YSBR-005]
  7. Fundamental Research Funds for the Central Universities from University of Science and Technology of China [WK2340000091, WK2060000018]

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In this study, we propose an efficient method to screen candidates with ultralow thermal conductivity in materials by analyzing the key spirit of the rattling model. Using a combination of structural information extraction and high-throughput computation, we identify 532 materials with ultralow thermal conductivity and verify the ultralow thermal conductivity of halide double perovskite structures.
Thermoelectric (TE) materials with rattling model show ultralow lattice thermal conductivity for high-efficient energy conversion between heat and electricity. In this work, by analysis of the key spirit of the rattling model, we propose an efficient empirical descriptor to realize the high-throughput screening of ultralow thermal conductivity in a series of semiconductors. This descriptor extracts the structural information of rattling atoms whose bond lengths with all the nearest neighboring atoms are larger than the sum of corresponding covalent radiuses. We obtain 1171 candidates from the Materials Project (MP) Database that contains more than 100 000 materials. Combining the empirical equation of high-throughput computation with a machine learning algorithm, we compute the approximate lattice thermal conductivities (kappa L) and find the kappa(L) values of 532 materials are less than 2.0 W m(-1) K-1 at 300 K, which can be regarded as the criteria of ultralow kappa(L) in general. In particular, we demonstrate that halide double perovskites structures show ultralow kappa(L), which provides valuable references for promising low kappa(L) materials in future experiments. In order to further verify our computational results, we calculate accurate kappa(L) for Rb2SnBr6 and CsCu3O2 as candidates with the low lattice thermal conductivity by solving the phonon Boltzmann transport equation. In particular, we demonstrate that Rb2SnBr6 has the lowest kappa(L) value of 0.1 W m(-1) K-1 at 300 K of all known thermal conductivity materials with the rattling model so far.

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