Papers
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Solvent Selection Scheme Using Machine Learning Based on Physicochemical Description of Solvent Molecules: Application to Cyclic Organometallic Reaction
Mikito Fujinami, Hiroki Maekawara, Ryota Isshiki, Junji Seino, Junichiro Yamaguchi, Hiromi Nakai
Bulletin of the Chemical Society of Japan 93 ( 7 ) 841 - 845 2020.07
5Citation(Scopus) -
Orbital-free density functional theory calculation applying semi-local machine-learned kinetic energy density functional and kinetic potential
Mikito Fujinami, Ryo Kageyama, Junji Seino, Yasuhiro Ikabata, Hiromi Nakai
Chemical Physics Letters 748 137358 - 137358 2020.06
19Citation(Scopus) -
Quantum Chemical Reaction Prediction Method Based on Machine Learning
Mikito Fujinami, Junji Seino, Hiromi Nakai
Bulletin of the Chemical Society of Japan 93 ( 5 ) 685 - 693 2020.05
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Virtual Reaction Condition Optimization based on Machine Learning for a Small Number of Experiments in High-dimensional Continuous and Discrete Variables
Mikito Fujinami, Junji Seino, Takumi Nukazawa, Shintaro Ishida, Takeaki Iwamoto, Hiromi Nakai
Chemistry Letters 48 ( 8 ) 961 - 964 2019.08
9Citation(Scopus) -
Development of Reaction Prediction Scheme Based on Machine Learning with Quantum Chemical Descriptors
Mikito FUJINAMI, Junji SEINO, Hiromi NAKAI
Journal of Computer Chemistry, Japan 15 ( 3 ) 63 - 65 2016
Click to view the Scopus page. The data was downloaded from Scopus API in June 05, 2023, via http://api.elsevier.com and http://www.scopus.com .