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Page 2 of 21 Chen et al. Energy Mater. 2025, 5, 500120 https://dx.doi.org/10.20517/energymater.2024.311

Keywords: Thermoelectric generator (TEG), materials, synthesis, optimization, design of experiment, machine
learning

INTRODUCTION
The ever-growing energy demand has become a global challenge due to the limited reserve of fossil fuels.
There have been considerable efforts to develop alternatives to fossil fuels, such as solar, nuclear, and wind.
Naturally, a lot of energy is wasted as heat in the modern world. Thermoelectric (TE) technologies, which
utilize the phenomena of energy conversion between heat and electricity, have the potential to reuse waste
heat for sustainable electrical energy generation . Nevertheless, TE technologies are environmentally
[1]
[2]
friendly renewable energy sources and have drawn tremendous attention over the last few decades .
Due to the ability to convert thermal to electrical energy and vice-versa, TE materials have potential in
[3]
various applications, including refrigeration, power generation, and waste heat recovery . Recently, there
have been plenty of research activities in the area of the use of statistical methods such as design-of-
experiment (DOE), machine learning (ML), and artificial intelligence (AI) in the area of TE technology with
a focus on the design of TE materials and thermoelectric generators (TEGs).

DOE is a statistical way of optimizing the response of experiments. Constructing experiments with the
minimum optimization parameter variations statistically to enhance the system's performance is crucial. AI
is a technology operating to do the task, which is constructed through data collection, model training,
optimization and deployment. It is used to find the new materials in the TE. ML belongs to AI, a statistical
model for optimization and finding the relation between input and output parameters. ML is developed
with mathematical models and algorithms for finding the pattern through statistical analysis of input data.
Gorai et al. have reviewed optimizing materials properties and designing and discovering TE materials
using ML . Wang et al. have given an overview of the use of several ML methods, such as Bayesian
[4]
optimization, regression, and neural network (NN) models in TE research . Chen et al. have reviewed the
[5]
use of ML in discovering and designing various materials for energy-related applications. This included ML
[6]
in photovoltaics, batteries, catalysis, and thermoelectric . Recatala-Gomez et al. reviewed the historical
evolution of various inorganic TE materials. They postulated that combining data generation, ML, high-
throughput synthesis and characterization, and high-performance computing can accelerate the discovery
[7]
of novel TE materials . Recently, Wang et al. have critically reviewed the progress on the application of ML
in (i) predicting and optimizing the properties (electrical and thermal transport) of TE materials; and (ii)
the designing and screening of TE materials . Furthermore, the optimization of TEGs based on statistical
[8]
approaches such as the Taguchi method, the response surface methodology (RSM), and the analysis of
[9]
variance (ANOVA) has been reviewed by Chen et al. . Kucova et al. reviewed waste heat harvesting from
the Internet of Things (IoT) through ML . The review concluded the suitability of TEG in low-grade waste
[10]
heat harvesting through the results of various ML algorithms. Song et al. draw the roadmap from high
[11]
throughput materials discovery to advanced device fabrication . Also, this review discussed the discovery
of new TE material through ML algorithms. Deng et al. discussed predictive ML algorithms based on the TE
materials . The previously reported reviews on TE mainly focused on new material discovery based on the
[12]
ML algorithm. One important part that was not discussed is atomic characteristics relation with TE
parameters and the parameter optimization in material synthesis through ML approach. However, these are
important to understand the TE material tailored to find a suitable approach to tune the TE performance
with minimal optimization parameters.

Regardless of the ability of ML and DOE to direct the discovery and optimization of TE materials and TEGs,
the ultimate ability to control the synthesis in a rational and controllable way will determine the research
and development of future TE technologies. However, the synthesis attempt of existing and unknown TE