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This work provides a data-oriented overview of the rapidly growing research field covering machine learning (ML) applied to predicting electrochemical corrosion. Our main aim was to determine which ML models have been applied and how well they performed depending on the corrosion topic considered. From an extensive review of corrosion articles presenting comparable performance metrics, a ‘Machine learning for corrosion database’ was created, guiding corrosion experts and model developers in their applications of ML to corrosion. Potential research gaps and recommendations are discussed, and a broad perspective for future research paths is provided.

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In suspected cases of COVID-19 infection, the World Health Organization has recommended the thorough cleaning of surfaces and the application of commonly used hospital-level disinfectants are effective procedures. The new virus situation has changed the sanitary habits of industries and this will potentially have anadverse effect on surfaces highly exposed to disinfectants that are often chlorinated. Therefore, the corrosion community should be concerned if the sudden increased use of disinfectants on certain surfaces would alter their corrosion rate/mechanism in the shortmedium term. This technical note also provides recommendations for forfuture studies to face these urgent surface challenges.

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