Generative Artificial Intelligence in Corrosion Inhibitor Discovery: A Critical Appraisal of Molecular Design, Quantum Descriptors and Electrochemical Validation
Anil Kumar *
P.G. Department of Chemistry, Sahibganj College Sahibganj, Jharkhand-816109, India.
*Author to whom correspondence should be addressed.
Abstract
Organic corrosion inhibitors remain central to the protection of metallic infrastructure, and the regulatory pressure to replace chromate-based systems has intensified the search for benign alternatives. Generative artificial intelligence, which proposes entirely new molecular structures rather than ranking existing ones, has recently entered this field and is frequently presented as a means of escaping the slow empirical screening cycle. This review critically evaluates what generative modelling has so far delivered for corrosion inhibitor discovery, with particular attention to three linked components: the molecular design architectures themselves, the quantum chemical and empirical descriptors used to condition and appraise them, and the electrochemical evidence offered in support of the resulting candidates. Literature was identified through structured searching of open scholarly indexes and citation registries, supplemented by backward and forward citation tracking and by inspection of journal platforms, and was appraised for methodological adequacy rather than citation frequency. Four findings emerge. Generative capacity has advanced far more rapidly than the experimental evidence required to justify it, and no published study has yet carried a genuinely de novo generated inhibitor through synthesis to standardised, time-resolved electrochemical assessment. The property models that steer generation are trained on small, literature-mined and internally heterogeneous datasets in which inhibition efficiency is treated as a molecular property rather than as a property of a metal–electrolyte–molecule system. Quantum chemical descriptors, although ubiquitous, carry weaker and less transferable predictive value than their prominence in the literature implies, and several careful studies report that single-parameter electronic correlations largely fail. Evaluation metrics imported from drug design measure chemical plausibility rather than corrosion relevance, and synthetic accessibility is seldom assessed. The field therefore possesses a credible generative toolkit, a fragile evidential base, and an unresolved translation problem. Priorities include system-aware target definitions, harmonised electrochemical reporting, negative-result deposition, uncertainty-aware conditioning, and prospective validation designs in which candidate selection precedes experiment.
Keywords: Corrosion inhibition, deep generative models, quantitative structure–property relationships, density functional theory descriptors, electrochemical impedance spectroscopy, molecular design, machine learning validation