Heusler
compounds form a diverse group of intermetallic materials
encompassing many compositions and structures derived from cubic prototypes,
and exhibiting complicated types of disorder phenomena. In particular,
preparing solid solutions between half-Heusler ABC and full-Heusler
compounds AB2C offers a means to control physical properties.
However, as is typical in materials discovery, they represent only
a small fraction of possible intermetallic compounds. To address this
problem of unbalanced data sets, a machine-learning model was developed
using an ensemble approach involving the synthetic minority oversampling
technique to predict new compounds likely to adopt half-Heusler structures.
The training set was based on experimental crystal structures, including
those of nonstoichiometric compounds. The model achieved an accuracy
of 98% on the validation set and gave excellent performance in terms
of balanced statistical measures. A subset of compounds predicted
to adopt half-Heusler structures having existing full-Heusler counterparts
was then targeted for preparation. Six of seven of these candidates
were successfully synthesized and confirmed to be half-Heusler compounds.