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Chen et al. Energy Mater. 2025, 5, 500120 Energy Materials
DOI: 10.20517/energymater.2024.311

Review Open Access

Statistical and artificial intelligence approaches

towards the optimization of thermoelectric materials
synthesis: a review

4
Wei-Hsin Chen 1,2,3,* , K. Aishwarya , Kripasindhu Sardar 1
1
Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 701, Taiwan.
2
Department of Chemical and Materials Engineering, Tunghai University, Taichung 407, Taiwan.
3
Department of Mechanical Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan.
4
Department of Physics, Vellore Institute of Technology, Chennai 600127, India.
*Correspondence to: Dr. Wei-Hsin Chen, Department of Aeronautics and Astronautics, National Cheng Kung University, No. 1,
University Rd., Tainan 701, Taiwan. E-mail: chenwh@mail.ncku.edu.tw
How to cite this article: Chen, W. H.; Aishwarya, K.; Sardar, K. Statistical and artificial intelligence approaches towards the
optimization of thermoelectric materials synthesis: a review. Energy Mater. 2025, 5, 500120. https://dx.doi.org/10.20517/
energymater.2024.311

Received: 31 Dec 2024 First Decision: 24 Feb 2025 Revised: 13 Apr 2025 Accepted: 27 Apr 2025 Published: 13 Jun 2025
Academic Editor: Yuhui Chen Copy Editor: Fangling Lan Production Editor: Fangling Lan

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.

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
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