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.