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

Abstract

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.

10 pages, 5 figures

References in corpus (10)

• BoltzTraP2, a program for interpolating band structures and calculating semi-classical transport coefficients
• Distribution of phonon lifetime in Brillouin zone
• Implementation strategies in phonopy and phono3py
• Discovery of low thermal conductivity compounds with first-principles anharmonic lattice dynamics calculations and Bayesian optimization
• Machine Learning and Evolutionary Prediction of Superhard B-C-N Compounds
• Data-driven Discovery of 3D and 2D Thermoelectric Materials
• Computational prediction of lattice thermal conductivity -- a comparison of molecular dynamics and Boltzmann transport approaches
• A Unified Understanding of Minimum Lattice Thermal Conductivity
• First-Principles Study of Strain Effect on Thermoelectric Properties of LaP and LaAs
• Machine learning the relationship between Debye temperature and superconducting transition temperature