Phase Behavior Predictions of Binary Linear Polymer Solutions using Machine Learning

The miscibility of polymers in solvents is important to many polymer processing applications including synthesis, purification, and self-assembly. Predicting the miscibility regions of linear polymer solutions with existing theories has been challenging however, and no universal model exists that quantitatively captures this behavior. Here, we show that a curated experimental data set consisting of 14 linear polymers and 46 solvents can be used to train machine learning models (random forest, XGBoost, and deep neural networks) to predict the cloud point temperature to within 3 °C, and capture various phase behaviors, ranging from upper and lower critical solubility curves, pressure effects (isopleths), and closed-loop behavior. The feature vector used as input is generalizable and consists of a combination of component and state descriptors. Determination of the relative importance of the various descriptors comprising the feature vector for the neural network model is consistent with prior knowledge of polymer phase behavior, including the critical role of polymer size, pressure, and concentration in determining an accurate cloud point temperature.