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Design of functional and sustainable polymers assisted by artificial intelligence

Nature Reviews Materials (2024)Cite this article

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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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Fig. 1: Polymer innovations over the past two centuries.
Fig. 2: Dielectric polymers for energy storage.
Fig. 3: Polymers for fuel cells.
Fig. 4: Polymers for Li-ion batteries.
Fig. 5: Polymers for the separation of gas mixtures.
Fig. 6: Polymers for the separation of complex liquid mixtures.
Fig. 7: Polymers for a sustainable world.

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Acknowledgements

The authors acknowledge support from several grants from the Office of Naval Research, the National Science Foundation and Toyota Research Institute, and a grant from the Department of Energy via the Center for Understanding and Controlling Accelerated and Gradual Evolution of Materials for Energy (UNCAGE-ME), an Energy Frontier Research Center under award no. DE-SC0012577.

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Authors and Affiliations

  1. School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Huan Tran, Chiho Kim & Rampi Ramprasad

  2. Matmerize Inc., Atlanta, GA, USA

    Huan Tran, Rishi Gurnani, Chiho Kim & Rampi Ramprasad

  3. GE Aerospace Research, Niskayuna, NY, USA

    Ghanshyam Pilania

  4. Toyota Research Institute, Los Altos, CA, USA

    Ha-Kyung Kwon

  5. School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Ryan P. Lively

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  1. Huan Tran
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Contributions

R.R. conceived and outlined the general manuscript. H.T. and R.R. wrote the initial manuscript with contributions from R.G., C.K., G.P., H.-K.K. and R.P.L. All authors edited the manuscript and figures and approved the final version for submission.

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Correspondence to Rampi Ramprasad.

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Tran, H., Gurnani, R., Kim, C. et al. Design of functional and sustainable polymers assisted by artificial intelligence. Nat Rev Mater (2024). https://doi.org/10.1038/s41578-024-00708-8

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