An interpretable stacking ensemble model for high-entropy alloy mechanical property prediction

High-entropy alloys (HEAs) have attracted significant attention due to their excellent mechanical properties and broad application prospects. However, accurately predicting their mechanical behavior remains challenging because of the vast compositional design space and complex multi-element interactions. In this study, we propose a stacking learning-based machine learning framework to improve the accuracy and robustness of HEA mechanical property predictions. Key physicochemical features were extracted, and a hierarchical clustering model-driven hybrid feature selection strategy (HC-MDHFS) was employed to identify the most relevant descriptors. Three machine learning algorithms-Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (Gradient Boosting)-were integrated into a multi-level stacking ensemble, with Support Vector Regression serving as the meta-learner. To improve model interpretability, the SHapley Additive Explanations (SHAP) method was applied to assess feature importance. The results demonstrate that the proposed stacking framework outperforms individual models in predicting yield strength and elongation, showing improved generalization ability and predictive accuracy.