Unsupervised machine learning for discovery of promising half-Heusler thermoelectric materials
Xue Jia, Yanshuai Deng, Xin Bao, Honghao Yao, Shan Li, Li Zhou, Chen Chen, Xinyu Wang, Jun Mao, Feng Cao, Jiehe Sui, Junwei Wu, Cuiping Wang, Qian Zhang, Xingjun Liu
Abstract
Abstract Thermoelectric materials can be potentially applied to waste heat recovery and solid-state cooling because they allow a direct energy conversion between heat and electricity and vice versa. The accelerated materials design based on machine learning has enabled the systematic discovery of promising materials. Herein we proposed a successful strategy to discover and design a series of promising half-Heusler thermoelectric materials through the iterative combination of unsupervised machine learning with the labeled known half-Heusler thermoelectric materials. Subsequently, optimized zT values of ~0.5 at 925 K for p-type Sc 0.7 Y 0.3 NiSb 0.97 Sn 0.03 and ~0.3 at 778 K for n-type Sc 0.65 Y 0.3 Ti 0.05 NiSb were experimentally achieved on the same parent ScNiSb.