Development of prediction model for cloud point of thermo-responsive polymers by experiment-oriented materials informatics

Abstract

Thermo-responsive polymers having a lower critical solution temperature (LCST) have attracted attention for biological applications such as drug delivery, diagnosis, and coating materials. In recent years, research on predicting LCST by utilizing machine learning has been conducted. However, since these methods targeted only copolymers combining specific monomer structures, they are not versatile, and multiple trials are still required to obtain new thermo-responsive polymers with the desired LCST. In this study, a prediction model for cloud point temperature (T_CP) was built by a combination of materials informatics and chemical insight, named sparse modeling for small data (SpM-S) using a small dataset of polymers collected from the literature as training data. This approach created a model that is interpretable, easy to calculate, and versatile. The prediction accuracy was validated using data from different literature sources and experimental test data. The model was able to predict the T_CP of polymers containing monomers not included in the dataset as well as polymers containing monomers included in the dataset. The predictive model has the potential to guide the design of new thermo-responsive polymers, and to contribute to efficient development of thermo-responsive polymers.