Recent advances in machine learning for the design and performance prediction of corrosion inhibitors

Recent advances in corrosion science and materials engineering have been significantly accelerated by the integration of artificial intelligence (AI), machine learning (ML), and highthroughput experimental techniques.This review and research compilation highlights the development of predictive models for corrosion assessment in metals, reinforced concrete, and biomedical alloys, combining non-destructive testing, electrochemical methods, and computational simulations.AI-driven approaches-including QSAR, gradient boosting, convolutional neural networks, and molecular dynamics-have been successfully applied to predict corrosion inhibition efficiency, detect structural degradation, and optimize inhibitor design.Case studies demonstrate the effectiveness of eco-friendly inhibitors such as guava leaf extract and Congo Red dye, hybrid nanocomposites, and quaternary ammonium salts, with experimental validation confirming high inhibition efficiencies.The integration of theoretical descriptors, imaging data, and deep learning facilitates rapid, accurate, and sustainable corrosion management, offering mechanistic insights into adsorption processes, surface protection, and fatigue behavior.These findings underscore the transformative potential of AI-assisted methodologies in accelerating corrosion research, inhibitor discovery, and structural health monitoring across diverse industrial and biomedical applications.