  Thermoelectric properties of Half Heusler alloys are predicted by adopting an
ensemble modelling approach, specifically the stacking model integrated using
Random Forest and XGBoost scheme. Leveraging a diverse dataset encompassing
thermal conductivity, the Seebeck coefficient, electrical conductivity, and the
figure of merit (ZT), the study demonstrates superior predictive performance of
the stacking Model, outperforming individual base models with high R2 values.
Key features such as temperature, mean Covalent Radius, and average deviation
of the Gibbs energy per atom emerge as critical influencers, highlighting their
pivotal roles in optimizing thermoelectric behavior. The unification of Random
Forest and XGBoost in the stacking model effectively captures nuanced
relationships, offering a holistic understanding of thermoelectric performance
in Half Heusler alloys. This work advances predictive modelling in
thermoelectricity and provides valuable insights for strategic material design,
paving the way for enhanced efficiency and performance in thermoelectric
applications. The ensemble modelling framework, coupled with insightful feature
selection and meticulous engineering, establishes a robust foundation for
future research in pursuing high-performance thermoelectric materials.
