A Machine Learning Study of Polymer-Solvent Interactions

Polymer-solvent interaction is a fundamentally important concept routinely described by the Flory-Huggins interaction ( χ ), Hildebrand solubility(✉ δ ) and the relative energy difference (RED) determined from Hansen solubility in experimental, theoretical and simulation studies. Here we performed a machine learning study based on a comprehensive and representative dataset covering the interaction pairs from 81 polymers and 1221 solvents. The regression models provide the coefficients of determination in the range of 0.86–0.94 and the classification models deliver the area under the receiver operating characteristic curve (AUCs) better than 0.93. These models were integrated into a newly developed software polySML-PSI. Important features including LogP, molar volume and dipole are identified, and their non-linear, nonmonotonic contributions to polymer-solvent interactions are presented. The widely known “like-dissolve-like” rule and two broadly used empirical equations to estimate χ as a function of temperature or Hansen solubility are also evaluated, and the polymer-specified constants are presented. This study provides a quantitative reference and a tool to understand and utilize the concept of polymer-solvent interactions.