Deep Learning Framework for Accurate Prediction and High-Throughput Search of the Thermoelectric Figure of Merit in Skutterudites

Abstract

The integration of artificial intelligence and machine learning is rapidly transforming the landscape of materials discovery, facilitating unprecedented acceleration in the exploration of vast chemical spaces and the prediction of material properties. However, the adoption of these advanced techniques has not been uniform across all subfields of materials science; thermoelectrics, for instance, has experienced a relatively slower penetration. This lag can be attributed to several inherent challenges, including the physical complexity of thermoelectric phenomena, the scarcity and reliability issues of available data, and limitations concerning the applicability of general models. To address these challenges, in this work, we have developed a machine learning model, based on neural networks, specifically for the accurate prediction of the thermoelectric figure of merit (zT ) in skutterudites. This model demonstrates high accuracy, with prediction errors approaching the range of experimental uncertainties reported for zT measurements. Furthermore, it offers the crucial capability of extracting design rules grounded in the underlying physics and chemistry of these materials, providing valuable insights for optimization. Most importantly, our model is applicable for the high-throughput screening of extensive chemical spaces, facilitating the efficient discovery of novel and high-performance thermoelectric materials.