Grain Boundary Segregation Engineering in Advanced Structural Alloys

Grain boundary segregation is a critical phenomenon governing the mechanical performance of advanced structural alloys, yet a comprehensive multi-scale understanding linking atomic-scale segregation mechanisms to macroscopic mechanical properties remains elusive. Here we present an integrated computational framework combining density functional theory (DFT), molecular dynamics (MD), phase-field simulations, crystal plasticity finite element modeling, and machine learning to elucidate the role of solute segregation on grain boundary stability and mechanical response. DFT calculations reveal that boron and carbon co-segregation at Σ3 twin boundaries reduces interfacial energy by 0.45 J/m² with a characteristic segregation width of 2.1 nm, driven by a non-equilibrium vacancy-mediated mechanism. MD simulations demonstrate that this segregated layer alters the dislocation nucleation pathway, increasing the critical resolved shear stress from 185 MPa to 278 MPa. Phase-field modeling of precipitation kinetics shows coarsening rates following LSW theory with a rate constant of 2.5 × 10⁻²⁷ m³/s at 1073 K, while CALPHADbased thermodynamic assessment establishes the temperature-dependent driving force for nucleation with a maximum ΔG_v = −45 J/mol at 900 K. Machine learning models trained on highthroughput DFT data achieve R² = 0.92 for yield strength prediction, identifying atomic size mismatch and valence electron concentration as dominant descriptors. The integrated model predicts that optimized grain boundary segregation combined with controlled precipitation yields a strength-ductility synergy exceeding 980 MPa with 24% elongation, validated against experimental measurements. This work establishes a predictive multi-scale framework for alloy design and demonstrates that atomic-scale control of grain boundary chemistry enables unprecedented combinations of strength and ductility.