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

Chia-Min Lin; Abishek Khatri; Da Yan; Cheng-Chien Chen

doi:10.3390/ma17215372

paper

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

arXiv:2408.06557 · doi:10.3390/ma17215372

Abstract

We perform machine learning (ML) simulations and density functional theory (DFT) calculations to search for materials with low lattice thermal conductivity, $I^º_{L}$ . 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 $I^º_{L}$ 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, $Z T$ , 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.

10 pages, 5 figures