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Design of polymers for energy storage capacitors using machine learning and evolutionary algorithms

  • Energy materials
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Abstract

To meet the demands of emerging electrification technologies, polymers that are capable of withstanding high electric fields at high temperatures are needed. Given the staggeringly large search space of polymers, traditional, intuition- and experience-based Edisonian approaches are too slow at discovering new polymers that can meet these demands. In this work, a genetic algorithm was combined with five machine learning-based property predictors to design over 50,000 hypothetical polymers that achieve target properties. Additionally, a polymer synthesizability-based criterion was used to narrow these polymers down to 23 candidates likely to be synthesizable and 3665 that may be synthesizable. A version of the genetic algorithm code is also made available for public use on GitHub.

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Code availability

A modified version of the genetic algorithm is available at: https://github.com/Ramprasad-Group/polyga

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Acknowledgments

This work is supported by the Office of Naval Research through a Multi-University Research Initiative (MURI) Grant (N00014-20-1-2586). We greatly appreciate it.

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Authors and Affiliations

  1. School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, GA, 30332, USA

    Joseph Kern, Lihua Chen, Chiho Kim & Rampi Ramprasad

Authors
  1. Joseph Kern
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  2. Lihua Chen
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  3. Chiho Kim
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  4. Rampi Ramprasad
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Contributions

JK: Writing and reviewing of manuscript, modifying of genetic algorithm, experiment design, and analysis. LC: Original designer of retrosynthesis algorithm, review and editing of the manuscript. CK: Original designer of genetic algorithm. RR: Supervision, Methodology, Funding acquisition, Resources, Writing—review & editing.

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Correspondence to Rampi Ramprasad.

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The authors declare that they have no conflict of interest.

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Handling Editor: Maude Jimenez.

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Kern, J., Chen, L., Kim, C. et al. Design of polymers for energy storage capacitors using machine learning and evolutionary algorithms. J Mater Sci 56, 19623–19635 (2021). https://doi.org/10.1007/s10853-021-06520-x

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