Citation: Parse, N.;
Pongkitivanichkul, C.; Pinitsoontorn,
S. Machine Learning Approach for
Maximizing Thermoelectric
Properties of BiCuSeO and
Discovering New Doping Element.
Energies 2022, 15, 779. https://
doi.org/10.3390/en15030779
Academic Editor:
Luis Hernández-Callejo
Received: 23 December 2021
Accepted: 19 January 2022
Published: 21 January 2022
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Article
Machine Learning Approach for Maximizing Thermoelectric
Properties of BiCuSeO and Discovering New Doping Element
Nuttawat Parse
1
, Chakrit Pongkitivanichkul
1
and Supree Pinitsoontorn
2,
*
1
Department of Physics, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand;
p.nuttawat@kkumail.com (N.P.); chakpo@kku.ac.th (C.P.)
2
Institute of Nanomaterials Research and Innovation for Energy (IN-RIE), Khon Kaen University,
Khon Kaen 40002, Thailand
* Correspondence: psupree@kku.ac.th
Abstract:
Machine learning (ML) has increasingly received interest as a new approach to accelerating
development in materials science. It has been applied to thermoelectric materials research for dis-
covering new materials and designing experiments. Generally, the amount of data in thermoelectric
materials research, especially experimental data, is very small leading to an undesirable ML model.
In this work, the ML model for predicting ZT of the doped BiCuSeO was implemented. The method
to improve the model was presented step-by-step. This included normalizing the experimental ZT of
the doped BiCuSeO with the pristine BiCuSeO, selecting data for the BiCuSeO doped at Bi-site only,
and limiting important features for the model construction. The modified model showed significant
improvement, with the R
2
of 0.93, compared to the original model (R
2
of 0.57). The model was
validated and used to predict the ZT of the unknown doped BiCuSeO compounds. The predicted
result was logically justified based on the thermoelectric principle. It means that the ML model can
guide the experiments to improve the thermoelectric properties of BiCuSeO and can be extended to
other materials.
Keywords: thermoelectric materials; thermoelectric properties; machine learning; BiCuSeO
1. Introduction
Electricity consumption is increasing continuously as a result of technological progress.
Thermoelectric is one of the interesting alternative energy technologies, which can convert
heat to electricity and vice versa. This technology provides many benefits, such as envi-
ronmentally friendly energy sources, scalability, and silent operation. Unfortunately, the
generic thermoelectric bulk modules perform with an efficiency of about 3–5% [
1
], which is
lower than other alternative energy sources such as solar cells with an efficiency of up to
30% [
2
]. In order to develop a better thermoelectric performance, thermoelectric materials,
the heart of the technology, need to be better developed. The key performance of ther-
moelectric materials is determined from the dimensionless Figure-of-Merit (ZT), defined
as
ZT =
S
2
σ
k
T [3]
where T,
σ
S, and k are the absolute temperature, electrical conductivity,
Seebeck coefficient, and thermal conductivity, respectively. Various methods have been
investigated to enhance ZT, and thus, the performance of the material.
Traditional approaches to investigate thermoelectric materials are by experiments and
computational methods based on density functional theory (DFT). In general, experiment-
ing requires expertise, instrument, and advanced technology, which consume considerable
resources. Furthermore, it is difficult to control overall variables and may require a long
acquisition period. Alternatively, the computational simulation needs less time and is
profitable in complete control over the essential variables. Nonetheless, there are also many
challenges for the DFT simulation related to microstructures of material. It needs high-
performance computing apparatus, usually in large computing clusters, which is difficult
Energies 2022, 15, 779. https://doi.org/10.3390/en15030779 https://www.mdpi.com/journal/energies
