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Machine learning enables polymer cloud-point engineering via inverse design

Author(s)
Kumar, Jatin N.; Li, Qianxiao; Tang, Karen Y. T.; Buonassisi, Anthony; Gonzalez-Oyarce, Anibal L.; Ye, Jun; ... Show more Show less
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Abstract
Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. Here we demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4 °C root mean squared error (RMSE) in a temperature range of 24–90 °C, employing gradient boosting with decision trees. The RMSE is >3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80 °C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers.
Date issued
2019-07
URI
https://hdl.handle.net/1721.1/130296
Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Journal
npj Computational Materials
Publisher
Springer Science and Business Media
Citation
Kumar, Jatin N. et al. "Machine learning enables polymer cloud-point engineering via inverse design." npj Computational Materials 5, 1 (July 2019): 73. © 2019 The Author(s)
Version: Final published version
ISSN
2057-3960

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