A Physics-Guided Surrogate Framework for Sustainability-Aware Screening of High-Entropy Alloys

The growing demand for energy-efficient transportation systems has intensified the need for structural materials that combine low density, high strength, and environmental responsibility. High-entropy alloys (HEAs), owing to their vast compositional flexibility and tunable mechanical properties, are promising candidates for next-generation lightweight structural applications. However, systematic experimental exploration of their expansive compositional design space remains time-consuming and resource-intensive. In this study, a physics-guided surrogate modeling framework is developed for sustainability-aware screening of HEA compositions under data-limited conditions. Starting from 32 experimentally reported alloys spanning FCC-, BCC-, and multiphase systems, a deterministic descriptor-driven strategy was employed to generate a physically consistent synthetic dataset across a 14-element compositional space. Key thermodynamic and atomic-scale descriptors, such as valence electron concentration (VEC), atomic size mismatch ([Formula: see text], configurational entropy ([Formula: see text]), electronegativity deviation ([Formula: see text]_std), mixing enthalpy proxies, and density-related features were incorporated to preserve metallurgical coherence. An XGBoost regression model trained on this physics-constrained dataset achieved strong internal consistency ([Formula: see text]) under controlled noise conditions, reflecting accurate reconstruction of the embedded descriptor–property relationships. Validation against an independent experimental literature dataset ([Formula: see text] alloys) yielded [Formula: see text], indicating physically meaningful transferability despite real-world microstructural and processing variability not explicitly captured by composition-based descriptors. Feature-importance and SHAP analyses consistently identified VEC, atomic size mismatch, and density-related terms as dominant contributors to yield strength, aligning with established solid-solution strengthening mechanisms. To extend the framework beyond mechanical optimization, a sustainability index based on elemental abundance, toxicity, and resource criticality was integrated into a composite eco-performance metric. The results demonstrate that strength-to-weight efficiency and environmental responsibility can be jointly optimized within the explored compositional domain. Overall, this work establishes a transparent and reproducible foundation for physics-informed, sustainability-aware HEA screening, positioning surrogate modeling as a structured compositional pre-screening tool to accelerate data-driven alloy design while maintaining alignment with metallurgical principles and sustainability objectives.