Towards Autonomous Corrosion Science: A Critical Review of Active Learning, Robotic Experimentation and Closed-Loop Electrochemical Discovery
Anil Kumar *
P.G. Department of Chemistry, Sahibganj College Sahibganj, Jharkhand-816109, India.
*Author to whom correspondence should be addressed.
Abstract
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.
Keywords: Autonomous experimentation, self-driving laboratory, active learning, Bayesian optimisation, high-throughput electrochemistry, corrosion inhibitor discovery, localised corrosion, materials informatics