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

We perform 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 A₂CdX (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 thermoelectric performance can exceed 1.0 in compounds such as K₂CdPb, K₂CdSn, and K₂CdGe, which are thereby also promising thermoelectric materials.