Machine learning enables polymer cloud-point engineering via inverse design
Name
s41524-019-0209-9.pdf
Description
Published version
Size
1.68 MB
Format
Adobe PDF
Checksum (MD5)
7d3ef846e4429cad1a6c0e5349770e7a
Author(s)
Kumar, Jatin N.
Li, Qianxiao
Tang, Karen Y. T.
Buonassisi, Anthony
Gonzalez-Oyarce, Anibal L.
Ye, Jun
Date Issued
July 2019
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
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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41524-019-0209-9
