Matsubara Kiho, Takahashi Kei, Matsuda Takeshi, Ueki Yuji, Seko Noriaki, Kakuchi Ryohei
Division of Molecular Science, Faculty of Science and Technology, Gunma University, 1-5-1 Tenjin, Kiryu, Gunma, 376-8515, Japan.
Faculty of Information Engineering, Fukuoka Institute of Technology, 3-30-1, Wajiro-higashi, Higashiku, Fukuoka, 811-0295, Japan.
Chempluschem. 2024 Apr;89(4):e202400061. doi: 10.1002/cplu.202400061. Epub 2024 Feb 5.
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.
本期封面邀请的是日本群马大学的粕口亮平博士和松原基保女士的合作团队、日本福冈工业大学和统计数学研究所的高桥圭教授、日本阪南大学的松田武教授、日本国立量子科学技术研究所的关晃明博士和植木雄二博士。封面图片展示了基于通过最先进的半经验方法计算出的单体实际信息，对乳液条件下辐射诱导接枝聚合进行的基于机器学习的优化和解释。更多信息可在松原基保、高桥圭、粕口亮平等人的研究论文中找到。
