Scientific Machine Learning for Coating Degradation and Corrosion Prediction: A Critical Review and Thermodynamic Research Perspective

Coating degradation and corrosion prediction are moving from empirical regression toward scientific machine learning (SciML), which embeds governing equations, geometry, and uncertainty into data-driven models. This critical review examines physics-informed neural networks (PINNs), neural operators, phase-field hybrids, graph neural networks, and probabilistic models relevant to protective coatings and localized corrosion. The synthesis identifies five persistent limitations: dimensionally inconsistent composite losses; incomplete enforcement of conservation and thermodynamic irreversibility; inadequate treatment of anomalous transport and material memory; loss of topological information during pit nucleation and coalescence; and weak out-of-distribution validation. The evidence is organized by governing mechanism, physical incorporation, geometric representation, uncertainty treatment, and validation design, and it is consolidated in a study-level comparison that distinguishes experimentally demonstrated capabilities from simulation-validated and conceptual ones across materials, coating classes, and corrosive environments. On this basis, this paper proposes a Thermo-Fractional Graph Phase-Field Neural Operator (TFG–PFNO) as a research framework that combines fractional memory, graph-based geometry, phase-field interface evolution, metriplectic dynamics, and calibrated uncertainty. The framework is a testable hypothesis rather than a validated model. Its principal implication is that credible corrosion digital twins must preserve physical admissibility and propagate uncertainty to structural risk, inspection planning, and maintenance decisions, rather than optimize interpolation accuracy alone.