A materials design pipeline consisting of two machine learning models was used to downselect and validate novel corrosion-resistant polymer structures for use in a high-temperature compatible protective coating. The first model is a generative model that creates polymers as candidates for coating applications. The second model is a graph neural network that predicts a variety of properties for the candidate polymers. Combined, these models create a database of over one million polymers with a variety of predicted properties; these polymers are ranked for their ability to serve as a protective coating and cross-referenced with existing literature and patents to validate the conclusions of the AI model. This resulted in producing one commercially available reagent, as well as two brand new structures with straightforward synthetic pathways, demonstrating that the generative AI has potential in the protective coating field as more extreme performance is required.

Subject
Protective coatings, Coatings, Materials, Materials performance, Machinery, Structures, Synthesis, Molecular structure, Polymers, Corrosion modeling, Corrosion protection, Computer based testing, Training
Keywords:
Artificial Intelligence, Informatics, Coatings, Polymers
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2025
Association for Materials Protection and Performance (AMPP)
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