From Density Functional Theory to Machine Learning Force Fields: A Critical Review of Multiscale Computational Modelling of Corrosion and Electrochemical Interfaces
Anil Kumar *
P.G. Department of Chemistry, Sahibganj College Sahibganj, Jharkhand-816109, India.
*Author to whom correspondence should be addressed.
Abstract
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.
Keywords: Corrosion modelling, machine learning force fields, density functional theory, electrochemical interfaces, passive films, multiscale simulation, uncertainty quantification