Artificial Intelligence (AI)-Driven Design of Multifunctional Smart Coatings for Corrosion Protection and Energy Applications: Mechanisms, Modelling, and Future Perspectives

The development of multifunctional smart coatings for corrosion resistance and energy-based applications has been highly boosted owing to the fast development of materials science and artificial intelligence. The conventional approach used in mitigating corrosion has mainly been empirical and trial and error, which may prove to be inefficient at times. Conversely, artificial intelligence (AI)-based methods have proved useful in developing predictive models and optimising the performance of the coatings. This review focuses on the current advancements made in AI-aided design strategies that combine machine learning and deep learning techniques. In particular, attention is devoted to the consideration of multipurpose, such as polymer-nanocomposites, coatings-reinforced by MXenes, and multipurpose materials for applications in both corrosion protection and energy systems. Synergistic combination of machine learning and experimental verification, which includes electrochemical experiments and surface characterisation, is also considered for establishing reliable structure-property correlation. Additionally, hybrid modelling using AI in combination with statistical analysis and density functional theory (DFT) is also mentioned. Despite many achievements, there are still problems associated with the availability of data, interpretability of the models used, and generalisability to other materials. This review aims to critically summarise recent advances in AI-driven multifunctional smart coatings while highlighting future research directions, including closed-loop optimisation, digital twins, self-driving research platforms, and advanced smart coatings.