Data-Driven Corrosion Science: Artificial Intelligence, Computational Chemistry and Predictive Electrochemistry

Corrosion prediction is intrinsically difficult because electrochemical kinetics, mass transport, evolving interfaces, material heterogeneity, mechanical loading and environmental histories interact across spatial and temporal scales. Conventional machine learning can extract useful predictive structure from corrosion datasets, yet its apparent accuracy often depends on narrow data distributions and may not survive extrapolation to new alloys, environments or damage states. Physics-informed artificial intelligence offers a different premise: mechanistic electrochemistry and continuum corrosion models can constrain learning, while data can identify parameters, close unresolved model components and update forecasts as observations accumulate. This critical narrative review evaluates that premise across corrosion science, scientific machine learning and uncertainty quantification. Literature published from 1 January 2015 to 21 July 2026 was examined, with foundational work included when needed to interpret phase-field modelling, physics-informed neural networks and probabilistic inference. The evidence indicates that the field is moving from physics-derived features and mechanistically structured surrogates towards neural solvers, inverse models and probabilistic hybrid digital representations. Recent corrosion-specific studies demonstrate promising data efficiency, parameter identification, domain generalisation and computational acceleration, but the strongest claims are still concentrated in numerical benchmarks, accelerated experiments and narrowly defined structural cases. Physics constraints do not automatically guarantee physical validity because governing equations may be incomplete, parameters may be non-identifiable and optimisation can satisfy residual losses while producing poorly calibrated predictions. Uncertainty quantification is therefore not an optional add-on but a central requirement for translating physics-informed learning into corrosion prognosis and risk-based decisions. Progress will depend on independent out-of-distribution validation, explicit separation of aleatoric, epistemic and model-form uncertainty, mechanistically identifiable architectures, multi-fidelity experimentation and shared benchmarks that test extrapolation rather than interpolation. Physics-informed artificial intelligence is best viewed as a disciplined framework for combining imperfect models with imperfect data, not as a replacement for electrochemical theory or validation.