Machine Learning Regression Guided Thermoelectric Materials Discovery – A Review

Guangshuai Han; Yixuan Sun; Yining Feng; Guang Lin; Na Lu

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ES Materials & Manufacturing

Volume 14, 2021

Machine Learning Regression Guided Thermoelectric Materials Discovery – A Review

Authors
Authors and affiliations

Guangshuai Han, Yixuan Sun, Yining Feng, Guang Lin and Na Lu

Guangshuai Han,^1,#

Yixuan Sun,^{2, #}

Yining Feng,¹

Guang Lin,^{2 3*Email}

Na Lu^{1, 3*Email}

¹Lyles School of Civil Engineering, Sustainable Materials and Renewable Technology (SMART) Lab, Purdue University, West Lafayette, IN 47906, USA

²School of Mechanical Engineering, Purdue University, West Lafayette, IN 47906, USA

³Center for intelligent infrastructure, Purdue University, West Lafayette, IN 47906, USA

Abstract

Thermoelectric materials have increasingly been given attention recently due to their potential of being a solid-state solution in converting heat energy to electricity. Good performing thermoelectric materials are expected to have high electrical conductivity and low thermal conductivity which are usually positively correlated. This poses a challenge in finding suitable candidates. Designing thermoelectric materials often requires evaluating material properties in an iterative manner, which is experimentally and computationally expensive. Machine learning has been regarded as a promising tool to facilitate material design thanks to its fast inference time. In this paper, we summarize recent progress and present the entire workflow in machine learning applications to thermoelectric material discovery, with an emphasis on machine learning regression models and their evaluation.

Machine Learning Regression Guided Thermoelectric Materials Discovery – A Review

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Publication details

Received: 07 Mar 2021
Revised: 04 Apr 2021
Accepted: 10 Apr 2021
Published online: 10 Apr 2021

Article type:
Review Paper

DOI:
10.30919/esmm5f451

Volume:
14

Page:
20-35

Citation:
ES Materials & Manufacturing, 2021, 14, 20-35

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Copyright

Number of downloads:
186

Citation Information:
4

Description:

This is the first comprehensive review to machine learning regression aided TE material discovery, w....

This article is cited by 4 publications.

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Bo He, Yinyan Xu, Jun Zhu, Xingyuan Zhang. Effects of the doping density of charge-transporting layers on regular and inverted perovskite solar cells: numerical simulations. Advanced Composites and Hybrid Materials 2021, 4, 1146, DOI: 10.1007/s42114-021-00343-9

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