Solvent selection scheme using machine learning based on physicochemical description of solvent molecules: Application to cyclic organometallic reaction

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.