Unsupervised machine learning for discovery of promising half-Heusler thermoelectric materials

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.