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Challenges and opportunities of polymer design with machine learning and high throughput experimentation

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Abstract

In this perspective, the authors challenge the status quo of polymer innovation. The authors first explore how research in polymer design is conducted today, which is both time consuming and unable to capture the multi-scale complexities of polymers. The authors discuss strategies that could be employed in bringing together machine learning, data curation, high-throughput experimentation, and simulations, to build a system that can accurately predict polymer properties from their descriptors and enable inverse design that is capable of designing polymers based on desired properties.

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Acknowledgments

J.N.K. and Q.L. are supported by the AME Programmatic Fund from the Agency for Science, Technology and Research under Grant No. A1898b0043. The concepts put forward in this paper were developed through discussions with Prof. Tonio Buonassisi, Dr. Kedar Hippalgaonkar, and Dr. Anibal L. Gonzalez-Oyarce.

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Authors and Affiliations

  1. Institute of Materials Research & Engineering, 2 Fusionopolis Way, #08-03, 138634, Singapore

    Jatin N. Kumar

  2. Institute of High-Performance Computing, 1 Fusionopolis Way, #16-16, 138632, Singapore

    Qianxiao Li & Ye Jun

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  1. Jatin N. Kumar
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Correspondence to Jatin N. Kumar.

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Kumar, J.N., Li, Q. & Jun, Y. Challenges and opportunities of polymer design with machine learning and high throughput experimentation. MRS Communications 9, 537–544 (2019). https://doi.org/10.1557/mrc.2019.54

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