GFN-xTB-Based Computations Provide Comprehensive Insights into Emulsion Radiation-Induced Graft Polymerization

Abstract

In this article, a deep insight into emulsion radiation-induced graft polymerization (RIGP) was obtained by computing explicit solvation free energies, conformational entropy, monomer radius and dipole moments with the state-of-the-art Conformer-Rotamer Ensemble Sampling Tool (CREST) package primarily at semiempirical GFN-xTB level. By leveraging the robustness of the CREST package, above parameters provided dynamic nature of methacrylate monomers with the consideration of realistic emulsion conditions. With the chemical and physical importance of the above results, CREST-determined explanatory variables sufficiently led to the building of the prediction models for the RIGP of methacrylate monomers. The machine learning model building resulted in effective reactivity predictions and unveiled important factors for the radiation-induced graft polymerization in a chemically interpretable fashion.

Keywords: GFN-xTB; emulsion conditions; explicit solvation models; machine learning; radiation-induced graft polymerization.