Machine Learning and First-Principle Predictions of Materials with Low Lattice Thermal Conductivity

机器学习和第一性原理预测低晶格热导率材料

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Abstract

We performed machine learning (ML) simulations and density functional theory (DFT) calculations to search for materials with low lattice thermal conductivity, κL. Several cadmium (Cd) compounds containing elements from the alkali metal and carbon groups including A(2)CdX (A = Li, Na, and K; X = Pb, Sn, and Ge) are predicted by our ML models to exhibit very low κL values (<1.0 W/mK), rendering these materials suitable for potential thermal management and insulation applications. Further DFT calculations of electronic and transport properties indicate that the figure of merit, ZT, for the thermoelectric performance can exceed 1.0 in compounds such as K(2)CdPb, K(2)CdSn, and K(2)CdGe, which are therefore also promising thermoelectric materials.

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