Data scientific research on desired solvent in organic reactions

Abstract

In experimental chemistry, a reaction condition optimization is performed to maximize yields of chemical products. Because there are various kind of conditions, the optimization process requires much labor and time. Recently, methods combined with machine learning have been developed to reduce the optimization costs especially for flow reactor systems. The purpose of this study is to develop a scheme to reduce the optimization costs in laboratory scale batch reactions using machine learning. First, we focused on a solvent condition, which affects yields largely. The present scheme utilizes several solvent properties as descriptors for machine learning. In this presentation, a real experimental dataset was adopted. We will show the analysis result about the correlation between solvent properties and experimental yields based on regression and clustering techniques.