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

Physics-Informed Artificial Intelligence for Corrosion Science: Bridging Mechanistic Electrochemistry, Machine Learning and Uncertainty Quantification

Anil Kumar — Mon, 05 Oct 2026 00:00:00 +0000

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.

From Density Functional Theory to Machine Learning Force Fields: A Critical Review of Multiscale Computational Modelling of Corrosion and Electrochemical Interfaces

Anil Kumar — Mon, 05 Oct 2026 00:00:00 +0000

Corrosion remains one of the most economically consequential materials-degradation processes, yet its rate-determining chemistry occurs within a few nanometres of a buried, electrified and chemically heterogeneous solid–liquid boundary that resists direct observation. Over the past two decades, density functional theory has supplied the dominant atomistic description of that boundary, furnishing adsorption energetics, defect formation and migration barriers, oxide surface chemistry and inhibitor binding trends. The computational expense of density functional theory nevertheless confines explicit simulation to a few hundred atoms and tens of picoseconds, which is several orders of magnitude removed from the length and time scales on which passive films break down and pits nucleate. Machine learning force fields, trained on first-principles reference data, have been advanced as the method that dissolves this constraint. This review critically evaluates how far that promise has been realised for corrosion and electrochemical interfaces, and where the evidence remains preliminary. Literature was identified through structured searching of open scholarly indexes and citation tracking, with every retained source verified against its registered bibliographic record. The synthesis indicates that machine learning force fields now reproduce first-principles structure and dynamics at oxide–water and metal–water interfaces with quantitative fidelity, and that constant-potential and finite-field formulations have begun to restore the electrode potential as a controllable simulation variable. Evidence is considerably weaker for the processes that actually govern corrosion: variable-valence cation dissolution, chloride-assisted passivity breakdown, long-range electrostatic coupling across the double layer, and the propagation of atomistic parameters into mesoscale and continuum models. Reported accuracies are dominated by energy and force errors on in-distribution test data, which are poor proxies for the electrochemical observables against which corrosion models must ultimately be judged. Progress therefore depends less on further architectural innovation than on potential-aware training data, uncertainty-aware deployment, and validation protocols anchored to measurable interfacial quantities.

Digital Twins for Corrosion Management: A Critical Appraisal of Electrochemical Sensing, Artificial Intelligence and Predictive Materials Degradation

Anil Kumar — Mon, 05 Oct 2026 00:00:00 +0000

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.

Artificial Intelligence and Electrochemical Impedance Spectroscopy: A Critical Appraisal of Automated Interpretation, Corrosion Diagnostics and Remaining Useful Life Prediction

Anil Kumar — Mon, 05 Oct 2026 00:00:00 +0000

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.

Generative Artificial Intelligence in Corrosion Inhibitor Discovery: A Critical Appraisal of Molecular Design, Quantum Descriptors and Electrochemical Validation

Anil Kumar — Mon, 05 Oct 2026 00:00:00 +0000

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.

Materials Informatics for Corrosion-Resistant Alloys: A Critical Review of Machine Learning, High-Throughput Computation and Composition–Microstructure–Performance Relationships

Anil Kumar — Mon, 05 Oct 2026 00:00:00 +0000

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.

Explainable and Trustworthy Artificial Intelligence in Corrosion Science: A Critical Review of Data Quality, Interpretability, Uncertainty and Mechanistic Consistency

Anil Kumar — Mon, 05 Oct 2026 00:00:00 +0000

Machine learning has become a standard analytical instrument in corrosion science, yet the transition from predictive demonstration to dependable engineering use remains incomplete. This review critically examines the four properties on which that transition depends: the quality and provenance of corrosion data, the interpretability of fitted models, the quantification and calibration of predictive uncertainty, and the consistency of learned relationships with established corrosion mechanisms. Literature was identified through structured searching of open scholarly indexes, supplemented by backward and forward citation tracking and by checks for corrections and retractions, with all bibliographic details and digital object identifiers verified against registration records. The synthesis indicates that trustworthiness in this field is frequently framed as a problem of model transparency, whereas the binding constraint lies earlier, in datasets assembled from heterogeneous exposure programmes, inconsistent electrochemical measurement practice and inconsistently defined degradation outcomes. Attribution-based explanation, overwhelmingly Shapley additive explanation, now accompanies most corrosion models, but its outputs are conditional on the fitted model and on strongly collinear environmental descriptors, so recovered feature rankings function as hypotheses rather than mechanistic findings. Uncertainty treatment is the least developed dimension: probabilistic outputs are reported in a minority of studies, calibration is rarely assessed, and performance under distribution shift, which is the operative condition for most asset applications, is seldom tested. Mechanistic consistency is generally assessed after fitting by comparing attributions with expert expectation, rather than imposed as a constraint, even though corrosion offers unusually firm kinetic and thermodynamic structure that could be encoded directly. Synthetic data augmentation, now common in small-sample corrosion studies, introduces a further circularity that current evaluation designs do not resolve. Progress requires provenance-complete and openly documented exposure datasets, explanation claims evaluated against mechanistic reference cases, calibrated and distribution-shift-aware uncertainty reporting, and architectures in which corrosion kinetics constrain rather than merely inform the learned function.

Towards Autonomous Corrosion Science: A Critical Review of Active Learning, Robotic Experimentation and Closed-Loop Electrochemical Discovery

Anil Kumar — Mon, 05 Oct 2026 00:00:00 +0000

Autonomous experimentation, in which machine learning selects experiments that robotic hardware then executes without human intervention, has produced rapid and well-documented gains in thin-film synthesis, nanocrystal manufacture, battery electrolyte formulation and electrocatalysis. Corrosion science has begun to import the same architecture, and platforms now exist that prepare electrolytes, deposit alloy libraries, run polarisation scans and fit impedance spectra under algorithmic control. This review examines whether that transfer is scientifically warranted, rather than whether it is technically feasible. Peer-reviewed journal literature was identified through a structured search of open scholarly databases and indexes, supplemented by backward and forward citation searching and by inspection of registration-agency records, with critical appraisal focused on measurement validity, statistical assumptions and the relationship between screening observables and service performance. Four arguments emerge. First, the autonomy achieved in corrosion-adjacent work is consistently lower than the language used to describe it, and high-throughput screening, laboratory automation and genuine closed-loop search are frequently conflated. Second, the observables that closed-loop search requires, namely fast, scalar, reproducible and monotonic objectives, are poorly matched to corrosion, whose engineering-relevant outcomes are slow, extreme-value governed, history dependent and strongly area dependent. Third, micro-electrochemical and combinatorial platforms purchase throughput by shrinking electrode area and exposure time, which systematically alters the sampled statistics of localised attack rather than merely adding noise. Fourth, the surrogate models and acquisition functions in current use were developed for well-behaved objectives and have rarely been adapted to heteroscedastic, censored and non-stationary corrosion measurements. Reported acceleration factors remain largely unbenchmarked, and interlaboratory reproducibility of standard electrochemical tests constrains any data-driven programme built upon them. The field's binding constraint is therefore the definition and validation of closed-loop-compatible corrosion observables, together with shared data infrastructure, rather than robotic capability. Progress will depend on multi-fidelity designs that connect rapid electrochemical proxies to slow exposure evidence, on noise-aware and censoring-aware algorithms, and on benchmark tasks that permit acceleration claims to be tested.