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 similar to 0.5 at 925 K for p-type Sc0.7Y0.3NiSb0.97Sn0.03 and similar to 0.3 at 778 K for n-type Sc0.65Y0.3Ti0.05NiSb were experimentally achieved on the same parent ScNiSb.

Automated summary

The combination of unsupervised machine learning with labeled known materials has led to the discovery and design of promising half-Heusler thermoelectric materials, with optimized zT values achieved experimentally.