Nanograined half-Heusler semiconductors as advanced thermoelectrics: an ab-initio high-throughput statistical study

TL;DR

This study uses ab-initio modeling and machine learning to identify promising nanograined half-Heusler compounds with high thermoelectric efficiency, surpassing many existing materials and revealing key compositional rules.

Contribution

It provides a large-scale computational screening of half-Heusler compounds for thermoelectric applications and uncovers simple predictive rules based on chemical composition.

Findings

Many compounds have ZT values above bulk counterparts.

Approximately 15% may outperform ZT~2 at high temperatures.

Machine learning reveals compositional rules for thermoelectric performance.

Abstract

Nanostructuring has spurred a revival in the field of direct thermoelectric energy conversion. Nanograined materials can now be synthesized with higher figures of merit (ZT) than the bulk counterparts. This leads to increased conversion efficiencies. Despite considerable effort in optimizing the known and discovering the unknown, technology still relies upon a few limited solutions. Here we perform ab-initio modeling of ZT for 75 nanograined compounds obtained by filtering down the 79,057 half-Heusler entries available in the AFLOWLIB.org repository according to electronic and thermodynamic criteria. For many of the compounds the $ZT$s are markedly above those attainable with nanograined IV and III-V semiconductors. About 15% of them may even outperform ZT~2 at high temperatures. Our analysis elucidates the origin of the advantageous thermoelectric properties found within this broad material class. We use machine learning techniques to unveil simple rules determining if a nanograined half-Heusler compound is likely to be a good thermoelectric given its chemical composition.