Machine Learning Regression Guided Thermoelectric Materials Discovery – A Review

Guangshuai Han, Lyles School of Civil Engineering, Sustainable Materials and Renewable Technology (SMART) Lab, Purdue University, West Lafayette, IN 47906, USA, Yixuan Sun, Yining Feng, Guang Lin, Na Lu, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47906, USA, Lyles School of Civil Engineering, Sustainable Materials and Renewable Technology (SMART) Lab, Purdue University, West Lafayette, IN 47906, USA, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47906, USA, Center for intelligent infrastructure, Purdue University, West Lafayette, IN 47906, USA, Lyles School of Civil Engineering, Sustainable Materials and Renewable Technology (SMART) Lab, Purdue University, West Lafayette, IN 47906, USA, Center for intelligent infrastructure, Purdue University, West Lafayette, IN 47906, USA
2021 ES Materials & Manufacturing  
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
more » ... 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.
doi:10.30919/esmm5f451 fatcat:2bt4knjqyngtjayhrzfxr2ezye