Crystal structure prediction in multifunctional materials such as Ni–Mn-based Heusler alloys is paramount to increasing applications in spintronics magnetic refrigeration and smart systems. This work provides a machine learning (ML) model for classifying these alloys’ austenitic and martensitic phases based on structural compositional and thermal properties. Several supervised learning models such as Decision Tree Naive Bayes k-Nearest Neighbors (kNN) and Artificial Neural Network (ANN) were trained and tested using a dataset compiled from experimental and first-principles literature. In order to enhance prediction accuracy, an ensemble classifier using majority voting was also proposed. The performance of the models was enhanced by feature selection and hyperparameter optimization. With 97.2% accuracy, the ensemble model outperformed any individual classifier. The results demonstrate the ability of machine learning (ML) to understand complex phase behaviors and offer a reliable route to accelerating the design and discovery of high-performing Heusler alloys.
Pal et al. (Wed,) studied this question.