Half-Heusler Structures with Full-Heusler Counterparts: Machine-Learning Predictions and Experimental Validation

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 AB(2)C 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.