Machine Learning Regression Guided Thermoelectric Materials Discovery – A Review
Guangshuai Han, Yixuan Sun, Yining Feng, Guang Lin and Na Lu
1 Lyles School of Civil Engineering, Sustainable Materials and Renewable Technology (SMART) Lab, Purdue University, West Lafayette, IN, 47906, USA
2 School of Mechanical Engineering, Purdue University, West Lafayette, IN, 47906, USA
3 Center 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.
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
Permissions:
Copyright
Number of downloads:
4375
Citation Information:
21
Description:
This is the first comprehensive review to machine learning regression aided TE material discovery, w....
This is the first comprehensive review to machine learning regression aided TE material discovery, which includes data generation, material descriptor selection, machine learning regression models, and model evaluation.
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