Prediction of Surface Free Energy of Polymer Surfaces by Machine Learning Modeling of Chemical Structure–Property Relationships

The wettability of liquids on polymer surfaces is an important issue in basic science and applications, and the ability of polymers to repel both water and oil with low surface free energy is considered useful for developing antifouling low-adhesion surfaces. Understanding the quantitative structure–property relationship for the wettability of polymer materials is essential for designing such polymer surfaces. Machine learning modeling can quantitatively analyze factors strongly correlated to the target properties of materials. In this study, a machine learning model for contact angle values for water and diiodomethane and surface free energy is presented to clarify the quantitative structure–property relationship for the wettability. This strategy not only predicts contact angles of the two types of liquids but also makes surface free energy predictable as a thermodynamic parameter from the molecular structure of polymers, where the surface free energy can be calculated using a theoretical model, the Owens–Wendt equation. A series of polymer brush surfaces with diverse chemical structures, including polymers with alkyl, silicone-like, and fluorinated side chains, were synthesized by surface-initiated atom transfer radical polymerization, and their wettability and surface free energies were evaluated. A data set was constructed using values for 72 types of polymer brush surfaces and bulk polymers collected from both our experiments and literature sources. The decision tree-based models were suitable to predict the water contact angle, diiodomethane contact angle, and surface free energy. The trained decision tree-based models were interpreted using Shapley additive explanation analysis and utilized to simulate the effect of the number of CH2 and CF2 groups in the side chains of hypothetical polymers. This machine learning model is expected to aid in designing water- and oil-repellent polymers and help with directly predicting the surface free energy from the chemical structure.