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Leveraging Artificial Intelligence for Accelerated Polymer Synthesis and Design
Summary
This review examines AI-enabled advances in polymer informatics, focusing on machine learning and deep learning approaches for accelerating the design of application-specific polymeric materials across energy storage, production, and sustainable economy applications including recyclable and biodegradable polymers. The review highlights how AI-powered workflows are shortening the design-to-discovery cycle for next-generation polymer materials.
Abstract Artificial intelligence (AI)-based methods continue to make inroads into accelerated materials design and development. Here, we review AI-enabled advances made in the subfield of polymer informatics, with a particular focus on the design of application-specific practical polymeric materials. We consider exemplar design attempts within a few critical and emerging application spaces, including materials designs for storing, producing and conserving energy, and those that can prepare us for a sustainable economy powered by recyclable and/or biodegradable polymers. AI-powered workflows help to efficiently search the staggeringly large chemical and configurational space of materials, using modern machine-learning (ML) algorithms to solve ‘forward’ and ‘inverse’ materials design problems. A theme explored throughout this Review is a practical informatics-based design protocol that involves creating a set of application-specific target property criteria, building ML model predictors for those relevant target properties, enumerating or generating a tangible population of viable polymers, and selecting candidates that meet design recommendations. The protocol is demonstrated for several energy- and sustainability-related applications. Finally, we offer our outlook on the lingering obstacles that must be overcome to achieve widespread adoption of informatics-driven protocols in industrial-scale materials development.
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