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.

在具有多个长度尺度的无序系统（例如聚合物）中，逆向设计是一个巨大的挑战，特别是在设计具有所需相行为的聚合物时。在这里，我们展示了通过机器学习对聚（2-恶唑啉）浊点进行高精度调节的过程。通过四个重复单元的设计空间和一定范围的分子质量，我们在24–90°C的温度范围内采用决策树的梯度增强技术，实现了4°C的均方根误差（RMSE）的精度。RMSE比线性和多项式回归好3倍以上。我们通过粒子群优化执行逆向设计，在37至80°C的4个目标浊点下，通过受限设计预测和合成17种聚合物。我们的方法通过机器学习算法来挑战聚合物设计的现状，