A Critical Review of Machine Learning Techniques on Thermoelectric Materials

Thermoelectric (TE) materials can directly convert heat to electricity and vice versa and have broad application potential for solid-state power generation and refrigeration. Over the past few decades, efforts have been made to develop new TE materials with high performance. However, traditional experiments and simulations are expensive and time-consuming, limiting the development of new materials. Machine learning (ML) has been increasingly applied to study TE materials in recent years. This paper reviews the recent progress in ML-based TE material research. The application of ML in predicting and optimizing the properties of TE materials, including electrical and thermal transport properties and optimization of functional materials with targeted TE properties, is reviewed. Finally, future research directions are discussed.

Automated summary

Thermoelectric materials have the potential to convert heat to electricity and vice versa, making them ideal for solid-state power generation and refrigeration. However, traditional methods of developing these materials are time-consuming and expensive. This paper reviews the recent progress in machine learning-based research on thermoelectric materials, including predicting and optimizing their properties and developing functional materials with targeted thermoelectric properties. The future research directions are also discussed.