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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)841-845
Number of pages5
JournalBulletin of the Chemical Society of Japan
Volume93
Issue number7
DOIs
Publication statusPublished - 2020 Jul

Keywords

  • Data science
  • Reaction condition optimization
  • Solvent selection

ASJC Scopus subject areas

  • Chemistry(all)

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