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

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.

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

Invited for this month's cover are the collaborating groups of Dr. Ryohei Kakuchi and Ms. Kiho Matsubara at Gunma University, Japan, Prof. Kei Takahashi at Fukuoka Institute of Technology and The Institute of Statistical Mathematics, Japan, Prof. Takeshi Matsuda at Hannan University, Japan, Dr. Noriaki Seko and Dr. Yuji Ueki at National Institutes for Quantum Science and Technology, Japan. The cover picture shows the machine learning-based optimization and interpretation of radiation-induced graft polymerizations under emulsion conditions based on realistic information for monomers calculated by the state-of-the-art semiempirical method. More information can be found in the Research Article by Kiho Matsubara, Kei Takahashi, Ryohei Kakuchi, and co-workers.