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

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_0.7Y_0.3NiSb_0.97Sn_0.03 and ~0.3 at 778 K for n-type Sc_0.65Y_0.3Ti_0.05NiSb were experimentally achieved on the same parent ScNiSb.