High-Throughput Screening of Aperiodic Superlattices Based on Atomistic Simulation-Informed Effective Medium Theory and Genetic Algorithm

SC Lin and YX Liu and ZL Cai and CY Zhao, INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, 202, 123694 (2023).

DOI: 10.1016/j.ijheatmasstransfer.2022.123694

Superlattices (SLs) have received great attention as thermoelectric materials because phonon transport in them can be tailored independently without changing the electronic properties to enhance the ther- moelectric figure of merit and conversion efficiency. Heterogeneous aperiodic SLs (ap-SLs) have lower thermal conductivity than their periodic counterparts (p-SL) due to Anderson localization of coherent phonons. Due to enormous amounts of layered structure combinations in ap-SLs, it is challenging to effi-ciently screen through all the combinations for the structure with the minimum thermal conductivity. In this work, a modified effective medium theory (m-EMT) is established to predict the thermal conductivity of Si/Ge p-SLs and ap-SLs. The effects of Anderson localization and layer thickness distribution are con- sidered using a correction function for the degree of randomization (DOR) in layer thickness. The combi-nation of the m-EMT and the genetic algorithm enables high-throughput screening of enormous amounts ( - 2 n , where n is the number of atomic layers) of SL structures with micron- scale total SL thickness. The thermal conductivities of ap-SLs can be constantly reduced to 1.5 W/(m center dot K) at average periodic layer thicknesses of 2.0 nm and a DOR of 0.8, regardless of the total SL thicknesses. Phonon spatial localization energy distribution further reflects the intense Anderson localization at interfaces, especially for those sandwiched between the highly asymmetric thick and thin layers. This work provides a high-throughput screening tool to theoretically design micron-scale and heterogeneous SLs, with detailed structural guid-ance for experimentally growing ap-SL with minimum thermal conductivities. (c) 2022 Elsevier Ltd. All rights reserved.

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