Statistical and artificial intelligence approaches towards the optimization of thermoelectric materials synthesis: a review

Abstract

Thermoelectric (TE) materials, capable of directly converting heat to electricity, offer a promising sustainable energy and waste heat recovery solution. Despite extensive research, a significant bottleneck remains: the synthesis of high-performance TE materials still relies heavily on trial-and-error approaches, which are time-consuming and resource-intensive. Moreover, while machine learning (ML) and design of experiments (DOE) have shown potential in optimizing synthesis processes across materials science, their systematic application to TE materials remains underexplored. In particular, very few reviews have addressed the integration of statistical and AI-guided methods for synthesizing and optimizing TE materials. This manuscript comprehensively reviews recent advances in statistical and artificial intelligence techniques for optimizing TE material synthesis. It first discusses the role of DOE in identifying critical synthesis parameters and explores various ML methods for predicting TE performance. This study then highlights case studies involving different TE material systems, synthesis strategies (e.g., ball milling, sputtering, electrodeposition), and ML-based performance prediction and optimization. This work fills a critical gap by linking data-driven optimization techniques with experimental synthesis in the TE field. It not only consolidates current knowledge but also sets the stage for future studies aiming to bridge material discovery and practical manufacturing. The insights presented are instrumental in accelerating the development of next-generation TE devices.

Keywords

Thermoelectric generator (TEG) / materials / synthesis / optimization / design of experiment / machine learning

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