Prediction of suitable solvents and non-solvents for polymers using machine learning techniques

Solvent selection is essential for formulations in industrial and research processes like paints, cosmetics and pharmaceuticals. Identifying appropriate solvents for a polymer formulation is usually done by trial-and-error, and therefore, is time-consuming. To mitigate this problem, quantitative measures of solvent-polymer miscibility known as solubility parameters have been developed in the past. In the present study, we first assessed the performance of the Hildebrand solubility parameter to predict solvents for a set of benchmark polymers. Machine learning techniques, trained on a dataset of known polymer Hildebrand solubility parameters, were then used to predict the solubility parameter of a queried polymer. Matching the predicted value with known solvent solubility parameters was then utilized to identify suitable solvents and non-solvents for the queried polymer. This capability has been implemented at www.polymergenome.org.