Machine learning enables polymer cloud-point engineering via inverse design

TL;DR

This paper demonstrates how machine learning can accurately predict and inverse-design polymers with specific cloud points, significantly improving over traditional regression methods and enabling rapid discovery of new polymer materials.

Contribution

The study introduces a machine learning approach using gradient boosting to precisely predict and inverse-design polymer cloud points, outperforming linear models and enabling systematic polymer discovery.

Findings

Achieved 4°C RMSE in cloud point prediction across 24-90°C range.

Successfully inverse-designed 17 polymers with targeted cloud points.

Outperformed linear and polynomial regression by over 3 times in accuracy.

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. 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 {\deg}C root mean squared error (RMSE) in a temperature range of 24-90 {\deg}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 {\deg}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.