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

Published online by Cambridge University Press:  03 May 2019

Jatin N. Kumar Open the ORCID record for Jatin N. Kumar [Opens in a new window] ,
Qianxiao Li  and
Ye Jun
Jatin N. Kumar*
Affiliation:
Institute of Materials Research & Engineering, 2 Fusionopolis Way, #08-03, 138634, Singapore
Qianxiao Li
Affiliation:
Institute of High-Performance Computing, 1 Fusionopolis Way, #16-16, 138632, Singapore
Ye Jun
Affiliation:
Institute of High-Performance Computing, 1 Fusionopolis Way, #16-16, 138632, Singapore
*
*Address all correspondence to Jatin N. Kumar at jatinkumar@mac.com and kumarjn@imre.a-star.edu.sg
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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.

Type
Artificial Intelligence Prospectives
Information
MRS Communications , Volume 9 , Issue 2 , June 2019 , pp. 537 - 544
Copyright
Copyright © Materials Research Society 2019 

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