Predicting binary solution phase behavior of polymers has remained a challenge since the early theory of Flory−Huggins, hindering the processing, synthesis, and design of polymeric materials. Herein, we take a complementary data-driven approach by building a machine learning framework to make fast and accurate predictions of polymer solution cloud point temperatures. Using polystyrene, both upper and lower critical solution temperatures are predicted within experimental uncertainty (1−2 °C) with a deep neural network, Gaussian process regression (GPR) model, and a combination of polymer, solvent, and state features. The GPR model also enables intelligent exploration of solution phase space, where as little as 25 cloud points are required to make predictions within 2 °C for polystyrene of arbitrary molecular weight in cyclohexane. This study demonstrates the effectiveness of machine learning for the prediction of liquid−liquid equilibrium of polymer solutions and establishes a framework to incorporate other polymers and complex macromolecular architectures.

The phase behavior of polymers in solution is crucial to many applications in polymer processing, synthesis, self-assembly, and purification. Quantitative prediction of polymer solubility space for an arbitrary polymer–solvent pair and across a large composition range is challenging. Qualitative agreement is provided by many current theoretical models, but only a portion of the phase space is quantitatively predicted. Here, we utilize a curated database for binary polymer solutions comprised of 21 linear polymers, 61 solvents, and 97 unique polymer–solvent combinations (6524 cloud point temperatures) to construct phase diagrams from machine learning predictions. A generalizable feature vector is developed that includes component descriptors concatenated with state variables and an experimental data descriptor (phase direction). The impact of several types of descriptors (Morgan fingerprints, molecular descriptors, and Hansen solubility parameters) to encode polymer–solvent interactions is assessed. Hansen solubility parameters are also introduced as a means to understand the general breadth of the linear polymer–solvent space as well as the density and distribution of curated data. Two common regression algorithms (XGBoost and neural networks) establish the generality of the descriptors; provide a root mean squared error (RMSE) within 3 °C for predicted cloud points in the test set; and offer excellent agreement with upper and lower critical solubility curves, isopleths, and closed-loop phase behavior by a single model. The ability to extrapolate to polymers that are very dissimilar from the curated data is poor, but with as little as 20 cloud points or a single phase boundary, RMSE error of predictions are within 5 °C. This implies that the current model captures aspects of the underlying physics and can readily exploit correlations to reduce required data for additional polymer–solvent pairs. Finally, the model and data are accessible via the Polymer Property Predictor and Database (3PDb).