Solvent Selection Scheme Using Machine Learning Based on Physicochemical Description of Solvent Molecules: Application to Cyclic Organometallic Reaction

• Mikito Fujinami

  Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555 , Japan

• Hiroki Maekawara

  Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555 , Japan

• Ryota Isshiki

  Department of Applied Chemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555 , Japan

• Junji Seino

  Waseda Research Institute for Science and Engineering (WISE), Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555 , Japan

• Junichiro Yamaguchi

  Department of Applied Chemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555 , Japan

• Hiromi Nakai

  Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555 , Japan

Abstract

A solvent selection scheme for optimization of reactions is proposed using machine learning, based on the numerical descriptions of solvent molecules. Twenty-eight key solvents were represented using 17 physicochemical descriptors. Clustering analysis results implied that the descriptor represents the chemical characteristics of the solvent molecules. During the assessment of an organometallic reaction system, the regression analysis indicated that learning even a small number of experimental results can be useful for identifying solvents that will produce high experimental yields. Observation of the regression coefficients, and both clustering and regression analysis, can be effective when selecting a solvent to be used for an experiment.

Journal

• Bulletin of the Chemical Society of Japan

  Bulletin of the Chemical Society of Japan 93 (7), 841-845, 2020-04-21

  Oxford University Press (OUP)

Keywords

• General Chemistry

Details

• CRID: 1360004236988327808
• NII Article ID: 130007879563
• DOI: 10.1246/bcsj.20200045
• ISSN: 13480634
• ISSN: 00092673
• Web Site: https://academic.oup.com/bcsj/article-pdf/93/7/841/56304495/bcsj.20200045.pdf
• Data Source:
  • Crossref
  • CiNii Articles
  • KAKEN