Physics-Informed Machine Learning for Sustainable Alloy Design: Toward a Recyclable Unified Q&P Steel

For sustainable alloy design, unified-composition approaches offer an effective route to deliver multiple performance levels while reducing chemistry complexity. Quenching and partitioning (Q&P) steels are widely used advanced high-strength steels, yet their grade development typically relies on distinct, grade-specific chemistries, complicating welding and hindering efficient recycling. This study proposes a physics-informed machine learning framework for unified-composition Q&P steel design, enabling multiple strength grades from a single alloy via heat-treatment tuning. A physics-guided property-bridging model integrates metallurgical descriptors with near-high-throughput hardness data, transferring knowledge from hardness to tensile properties under sparse tensile labels. This approach enables improved prediction of tensile strength and elongation from limited tensile datasets. A multi-objective genetic algorithm then explores the composition-process space to identify one alloy that meets the Q&P980, Q&P1180, and Q&P1380 grade targets via processing adjustments. Compared to a purely data-driven baseline, the framework substantially improves tensile prediction accuracy (R² up to 84% vs. 74%) while maintaining better model stability under data-sparse conditions. An experimental validation demonstrates that the same chemistry can achieve ∼980, ∼1180, and ∼1380 MPa tensile strengths with suitable ductility under different Q&P schedules. Overall, the physics-informed framework exemplifies a paradigm for sustainable alloy design that reduces chemistry variants while simplifying recycling.