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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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
2019-07Department
Massachusetts Institute of Technology. Department of Mechanical EngineeringJournal
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