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Thermoelectric Prediction from Material Descriptors Using Machine Learning Technique

Thermoelectric Prediction from Material Descriptors Using Machine Learning Technique

Original Research ArticleMay 10, 2023Vol. 23 No. 6 (2023) https://doi.org/10.55003/cast.2023.06.23.014

Abstract

In this work, we employed a machine learning framework to predict the thermoelectric power factors of materials based on their composition and structure. To generate a broad range of materials for analysis, we sourced an existing dataset from the Materials Project database. The electronic transport properties, which serve as the output variables, were obtained from the same database via a Boltzmann transport theory calculation beyond ab-initio method. These properties were used to generate input data, or material descriptors, which rely solely on atomic information and crystal structure without recourse to density functional theory calculations. The descriptors were transformed into numerical features using the open-source software Matminer. Non-linear machine learning regression models were trained and tested on the transformed datasets, and their performance was evaluated. The optimized random forest model produced the most accurate predictions, with a yield of 88%. The ultimate goals of this research were to develop material selection strategies that bypass the need for self-consumption in density functional theory calculations, and to demonstrate the potential of machine learning models to describe the thermoelectric properties of existing materials datasets.

Keywords: thermoelectric; machine learning; neural network; random forest

*Corresponding author: Tel.: (+66) 869020207

                                             E-mail: kittiphong.am@kmitl.ac.th

References

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Ward, L., Agrawal, A., Choudhary, A. and Wolverton, C., 2016. A general-purpose machine learning framework for predicting properties of inorganic materials.npj Computational Materials, 2, DOI: 10.1038/npjcompumats.2016.28.
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Curtarolo, S., Hart, G.L.W., Nardelli, M.B., Mingo, N., Sanvito, S. and Levy, O., 2013. The high-throughput highway to computational materials design. Nature Material, 12, 191-201, DOI: 10.1038/nmat3568.
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Xi, L., Pan, S., Li, X., Xu, Y., Ni, J., Sun, X., Yang, J., Luo, J., Xi, J., Zhu, W., Li, X., Jiang, D., Dronskowski, R., Shi, X., Snyder, G.F. and Zhang, W., 2013. Discovery of high-performance thermoelectric chalcogenides through reliable high-throughput material screening. Journal of the American Chemical Society, 140, 10785-10793, DOI: 10.1021/jacs.8b04704.
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Author Information

Pakawat Sungphueng

College of Materials Innovation and Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

Kittiphong Amnuyswat*

College of Materials Innovation and Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

About this Article

Journal

Vol. 23 No. 6 (2023)

Type of Manuscript

Original Research Article

Keywords

thermoelectric;
machine learning;
neural network;
random forest

Published

10 May 2023

Current Journal

Journal Cover
Vol. 23 No. 6 (2023)

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