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, ${\kappa }_L$. 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 ${\kappa }_L$ 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 K${}_2$CdPb, K${}_2$CdSn, and K${}_2$CdGe, which are thereby also promising thermoelectric materials.