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

Corrosion-resistant alloys are developed in composition spaces too large for empirical exploration, and the promise of materials informatics is that statistical learning, high-throughput computation and rapid experimentation together can compress decades of alloy development into manageable campaigns. Progress over the past decade has been substantial, yet the field has matured unevenly, and the confidence that can reasonably be placed in its predictions varies sharply with the degradation mode under consideration. This review examines the evidence critically rather than cataloguing it. Literature was identified through systematic searching of open scholarly indexes, supplemented by citation tracing and targeted searching of specialist corrosion sources, and appraised for methodological adequacy, target definition, validation design and the strength of experimental confirmation. Five interlocking problems organise the synthesis. The prediction target in corrosion is not a single material property but a family of measurement-dependent observables whose relationship to service performance is indirect, which makes model comparison across studies far less meaningful than reported error statistics suggest. Training data are dominated by small, environment-specific, non-standardised compilations in which reporting conventions vary and negative results are largely absent. Compositional descriptors dominate model inputs, so that processing route and microstructure, which frequently govern localised attack, enter models only implicitly. High-throughput computation supplies thermodynamic and surface-energetic quantities of genuine value but does not supply the kinetic quantities that determine passivity breakdown. Validation practice relies predominantly on random data splits that reward interpolation and disguise the extrapolative task that alloy discovery actually poses. The evidence is strongest where degradation is quasi-uniform, environmental variables are measured and the chemistry is dilute, and weakest for localised attack in complex multi-principal element alloys. Closed-loop workflows that couple prediction to rapid synthesis and testing offer the most credible route to improvement, provided that their design claims are held to experimental confirmation rather than to cross-validation performance alone.