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

Corrosion remains one of the most persistent degradation processes affecting metallic infrastructure, and the prospect of managing it through digital twins has attracted rapid interest across the energy, marine, transport and construction sectors. A digital twin is commonly presented as a virtual replica that is continuously updated by measurements from its physical counterpart and that returns actionable predictions. Applied to corrosion, this proposition requires three capabilities to operate together: sensing that yields interpretable measures of electrochemical state, models that convert those measures into credible forecasts of material loss or cracking, and decision logic that converts forecasts into inspection and maintenance actions. This review critically examines how far the published evidence supports that integration. Peer-reviewed literature published between January 2015 and July 2026 was identified through open scholarly indexes and citation searching, appraised for methodological adequacy, and synthesised thematically rather than catalogued. The evidence indicates that individual components are technically mature in isolation. Electrochemical noise, impedance and resistance-based sensing can resolve corrosion processes under laboratory and, increasingly, field conditions; machine learning reproduces corrosion rates with good apparent accuracy within the range of its training data; and probabilistic updating frameworks are well established in structural reliability. The integration itself is far less well evidenced. Most systems described as corrosion digital twins are unidirectional monitoring architectures without demonstrated feedback to the virtual model, validation is dominated by short-duration or accelerated tests, uncertainty is rarely propagated from sensor to decision, and independent replication across assets is almost absent. The strongest sectoral evidence concerns offshore wind support structures, pipelines and marine hulls, while reinforced concrete and high-temperature systems remain comparatively underserved. Priorities include long-duration field validation, uncertainty-aware model updating, transparent reporting of extrapolation limits, and decision-referenced performance metrics rather than regression accuracy alone.