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• W4287981504 endingPage "12741" @default.
• W4287981504 startingPage "12735" @default.
• W4287981504 abstract "Machine learning techniques such as support vector regression (SVR) and artificial neural network (ANN) algorithms are employed to screen binary barium family (BamXn) compounds with low lattice thermal conductivity. To this aim, the electronic structures and thermoelectric (TE) properties of Ba2X6 (X = S, Se, Te) are studied utilizing the first-principles density functional theory (DFT) together with the semiclassical Boltzmann transport theory (SBTT). The results indicate that Ba2Se6 and Ba2Te6 exhibit low lattice thermal conductivity (∼0.26 and 0.31 W m–1 K–1) at room temperature. The electronic structure calculations show that three Ba2X6 (X = S, Se, Te) compounds are indirect band gap semiconductors with gap values of 1.37, 0.84, and 0.92 eV, respectively. The TE calculations show that the maximum ZT value of Ba2Te6 can be up to 3.51 at 800 K and 1.4 at 300 K. Our results indicate that all Ba2X6 (X = S, Se, Te) host good TE performances covering wide temperature ranges and have potential applications. In addition, the developed methods in this work may help to further search other materials of high TE performances." @default.
• W4287981504 created "2022-07-26" @default.
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• W4287981504 date "2022-07-26" @default.
• W4287981504 modified "2023-10-16" @default.
• W4287981504 title "Theoretical Study of Ba₂X₆ (X = S, Se, Te) for Thermoelectric Applications Based on First-Principles Calculations and Machine Learning" @default.
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• W4287981504 doi "https://doi.org/10.1021/acs.jpcc.2c00880" @default.
• W4287981504 hasPublicationYear "2022" @default.
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