  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.
