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Marine biofouling has significant economic and environmental consequences, including increased drag, higher fuel consumption, elevated emissions, additional maintenance costs and accelerated material degradation. Despite progress in sustainable antifouling coatings, their development still relies largely on trial-and-error approaches. The author argues that recent advances in artificial intelligence, machine learning and materials informatics could accelerate the discovery and optimisation of such coatings by predicting performance, identifying structure-property relationships and reducing experimental effort.
However, the direct application of AI and ML specifically to marine antifouling coating design remains relatively limited. Much of the available evidence originates from broader materials science and coatings research, requiring careful evaluation when transferring approaches to this complex application area.
Reading tip: Corrosion protection coatings
The book “Anticorrosive Coatings: Fundamentals and New Concepts” brings together all the disciplines involved in the creation and use of corrosion protection coatings for metals. It presents the latest insights into the quality and chemistry of surfaces, the proper way to prepare them by conversion treatment, the function of resins and anticorrosion pigments in paints, and novel concepts in corrosion protection. As an added bonus, all the standards and directives mentioned in the text are grouped together in their own chapter for a better overview.
Current evidence and key challenges
The review critically distinguishes between directly demonstrated antifouling applications and transferable approaches from related materials systems. It focuses on how AI and ML can be applied to predict fouling resistance, durability, corrosion protection and environmental sustainability, and evaluates the extent to which these approaches can be translated to marine antifouling systems. Key challenges identified include data scarcity, heterogeneous experimental datasets, the absence of standardised testing protocols, limited field validation and the need for improved model interpretability.
Toward autonomous materials discovery
Future opportunities highlighted in the review include digital twins, explainable AI, generative AI and autonomous materials discovery platforms as enablers of next-generation sustainable antifouling coatings. The author proposes a staged pathway from standardised data generation and predictive modelling toward experimental validation, active learning and ultimately autonomous materials discovery. According to the review, establishing reliable AI-assisted design frameworks will require coordinated efforts across data generation, protocol standardisation and model development, offering the coatings community a structured roadmap for integrating digital tools into the development of more sustainable marine coatings.
