Data-driven prediction and classification of multi-component alloys using interpretable machine learning

Abstract

The design of advanced metallic alloys is challenged by complex, nonlinear interactions among multiple alloying elements, making conventional trial-and-error approaches costly and time-intensive. This study presents an integrated, interpretable machine learning framework applied to a dataset of 2,672 multi-component alloy systems, using elemental composition as the sole input. A multi-output Random Forest Regressor simultaneously predicts Ultimate Tensile Strength (UTS) and Liquidus Temperature, achieving a test R² of 0.848 and a 5-fold cross-validation R² of 0.849 ± 0.017, outperforming Linear Regression (R² = 0.512) and Gradient Boosting (R² = 0.787) baselines. A Logistic Regression classifier identifies high-performance alloy compositions defined by simultaneous UTS and liquidus temperature thresholds achieving an overall accuracy of 82% and an AUC of 0.871. Threshold sensitivity analysis confirms classification stability across varying performance criteria. Principal Component Analysis (PCA) and K-Means clustering, applied to the full compositional feature space, reveal three distinct alloy families with systematic differences in mechanical and thermal properties. Critically, interpretability is preserved throughout via SHAP analysis and permutation-based feature importance, identifying Vanadium (V), Iron (Fe), Tungsten (W), and Carbon (C) as the dominant compositional drivers a finding that both confirms and extends established metallurgical understanding. The dataset was verified to contain no missing values across all 31 elemental features. Collectively, the proposed framework provides a scalable and interpretable foundation for accelerated alloy screening, property prediction, and data-driven materials discovery.