Machine Learning Regression Guided Thermoelectric Materials Discovery – A Review

Guangshuai Han,1,#

Yixuan Sun,2, #

Yining Feng,1

Guang Lin,2 3*Email

Na Lu1, 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