LAUSR

Thermoelectric (TE) materials provide a solid‐state solution in waste heat recovery and refrigeration. During the past few decades, considerable effort has been devoted towards improving the performance of TE materials, which requires the optimization of multiple interrelated properties. A fundamental understanding of the interaction processes between the various energy carriers, such as electrons and phonons, is critical for advances in the development of TE materials. However, this understanding remains challenging primarily due to the inaccessibility of time scales using standard atomistic simulations. Machine learning methods, well known for their data‐analysis capability, have been successfully applied in research on TE materials in recent years. Here, an overview of the machine learning methods used in thermoelectric studies is provided, with the role that each machine learning method plays being systematically discussed. Furthermore, to date, the scale of thermoelectric‐related databases is much smaller than those in other fields, such as e‐commerce, image identification, and speech recognition. To overcome this limitation, possible strategies to utilize small databases in promoting materials science are also discussed. Finally, a brief conclusion and outlook are presented.