Polymer design using machine learning: A quest for high glass transition temperature

Abstract

This research introduces an advanced data-centric framework tailored for polymer design. Developing a machine learning (ML) model utilizing molecular descriptors, the study aims to forecast the glass transition temperature of polymers. Employing the Retrosynthetically Interesting Chemical Substructures (BRICS) technique, a set of 10,000 novel polymers is synthesized, followed by predicting their glass transition temperature (Tg) values using a pre-existing ML model trained for this purpose. Gradient Boosting and Extra Trees models outperform K Neighbors and Random Forest (KNN) models in predicting performance parameters with R2 scores of 0.99 and 0.99, and lower root mean squared error values of 24 and 32 K, respectively. The selection of polymers is done on the basis of glass transition temperature, those with higher glass transition temperature are selected. We evaluate the ease of synthetic accessibilities of the selected polymers alongwith their structural variations which indicates a diverse range among them. Through the successful identification and enhancement of novel polymers, these innovative methods greatly enhance the possibilities of discovering superior materials tailored for cutting-edge applications.