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

Electrochemical impedance spectroscopy is one of the few measurements capable of resolving interfacial charge transfer, film properties and mass transport in a single non-destructive experiment, yet its industrial uptake has been constrained for decades by an interpretation step that remains expert-dependent, slow and partly subjective. A substantial body of work now proposes artificial intelligence as the solution, spanning supervised classification of equivalent circuit topologies, Bayesian and evolutionary model search, neural and Gaussian-process deconvolution of relaxation-time distributions, unsupervised clustering of degradation states, and end-to-end prediction of corrosion condition and remaining useful life. This review evaluates that literature critically rather than cataloguing it, and asks whether the reported advances address the constraint that actually limits impedance interpretation. Sources were identified through structured searching of open scholarly indexes, supplemented by backward and forward citation tracking, with all bibliographic details and digital object identifiers verified against registry records. Three findings dominate the synthesis. First, most automation effort has been directed at circuit selection and parameter estimation, whereas the binding constraint is the non-uniqueness of impedance models, which no amount of classification accuracy resolves; algorithms trained on a labelled circuit library reproduce the conventions of that library rather than adjudicating physical mechanism. Second, the evidence base is markedly asymmetric: battery prognostics rests on large, experimentally acquired and partly public spectral datasets with an unambiguous end-of-life criterion, while corrosion diagnostics relies heavily on simulated spectra, small specimen counts and proxy failure definitions, so successes in the former do not transfer as precedent to the latter. Third, validity screening, uncertainty reporting and out-of-distribution testing remain inconsistent, and measurement-side confounds such as electrode geometry and non-stationarity are rarely represented in training data. Confidence is therefore highest for constrained state estimation under controlled conditions and lowest for field corrosion prognosis. Priorities include shared corrosion benchmarks, mechanistically grounded validation and honest reporting of predictive limits.