Multi-objective optimization of corrosion resistance, strength, ductility properties of weathering steel utilizing interpretable attention-based deep learning model

A high-performance, low-cost weathering steel was developed using a deep learning-based interpretable Attention mechanism, which identifies key compositional factors and captures complex feature interactions. By revealing the intrinsic drivers of strength, ductility, and corrosion resistance, and introducing a utility function to balance performance and cost, the newly designed steel achieved a UTS of 837 MPa, 20% elongation, and a corrosion rate of 0.54 g/(m2·h) after 576 hours in a simulated marine environment, demonstrating excellent overall properties.