Machine learning enables polymer cloud-point engineering via inverse design
This work addresses the problem of polymer design for materials science, offering a fast and systematic discovery method, though it is incremental in applying machine learning to a specific domain.
The researchers tackled the challenge of inverse design for polymers with desired phase behavior, specifically tuning poly(2-oxazoline) cloud points, achieving a 4 °C root mean squared error accuracy, which is over 3 times better than traditional regression methods.
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.