A Critical Review of Machine Learning Techniques on Thermoelectric Materials

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.

High-Throughput Strategies in the Discovery of Thermoelectric Materials

Searching for new high-performance thermoelectric (TE) materials that are economical and environmentally friendly is an urgent task for TE society, but the advancements are greatly limited by the time-consuming and high cost of the traditional trial-and-error method. The significant progress achieved in the computing hardware, efficient computing methods, advance artificial intelligence algorithms, and rapidly growing material data have brought a paradigm shift in the investigation of TE materials. Many electrical and thermal performance descriptors are proposed and efficient high-throughput (HTP) calculation methods are developed with the purpose to quickly screen new potential TE materials from the material databases. Some HTP experiment methods are also developed which can increase the density of information obtained in a single experiment with less time and lower cost. In addition, machine learning (ML) methods are also introduced in thermoelectrics. In this review, the HTP strategies in the discovery of TE materials are systematically summarized. The applications of performance descriptor, HTP calculation, HTP experiment, and ML in the discovery of new TE materials are reviewed. In addition, the challenges and possible directions in future research are also discussed.