Neural Network Prediction of Polymer-Solvent Coexistence Curves

Solution phase behavior of polymers is fundamental to synthesis, processing, purification, and self-assembly. While there have been numerous attempts at predicting phase behavior from Flory-Huggins theory and equation of state methods, no such universal theory exists, and often additional fitting parameters are needed. Taking an orthogonal approach, we establish the feasibility of using experimental data and machine learning algorithms (neural networks, Gaussian Process Regression, and others) to predict polymer phase behavior. Focusing on available data (>2500 cloud points) on polystyrene in various solvents, we examine the precision of upper and lower critical solution co-existence predictions with various feature descriptors. We show that these models can predict cloud point temperatures for unknown concentrations, molecular weights, and solvents within experimental error. Furthermore, these models can be used to estimate unknown polymer-solvent properties, such as Chi, or Hansen solubility parameters from molecular “fingerprints”. This methodology demonstrates the potential to establish a community database, which can scale with user input (or automated data collection) and integrate prior knowledge to provide phase behavior estimates, as well as test theoretical concepts.