Integrated manufacturing of REciclable multi-material COmposites for the TRANSport sector
Machine Learning and First-Principle Predictions of Materials with Low Lattice Thermal Conductivity
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 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 the thermoelectric performance can exceed 1.0 in compounds such as K2CdPb, K2CdSn, and K2CdGe, which are therefore also promising thermoelectric materials.
» Author: Chia-Min Lin
» Reference: doi: 10.3390/ma17215372
» Publication Date: 02/11/2024
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 768737
