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kumar_et_al_polymer_full.pdf (1.79 MB)

Machine Learning Enables Polymer Cloud-Point Engineering via Inverse Design

submitted on 28.12.2018, 05:27 and posted on 28.12.2018, 16:33 by Jatin Kumar, Qianxiao Li, Karen Y.T. Tang, Tonio Buonassisi, Anibal L. Gonzalez-Oyarce, Jun Ye

Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. 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.


Email Address of Submitting Author


Institute of Materials Research & Engineering



ORCID For Submitting Author


Declaration of Conflict of Interest



Read the published paper

in npj Computational Materials