  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.
