[24a-E203-1~10] 23.1 Joint Session N "Informatics"

△ [24a-E203-4] Predicting solubility of organic compounds in toluene using transfer learning

〇Tomoharu Okada¹, Matsui Hiroyuki¹ (1.Yamagata Univ.)

Keywords:machine learning, organic semiconductor, graph convolution neural network

In the field of organic electronics, fabrication of sensors and circuits by printing process has been studied. Solubility is important property when ink of organic semiconductors is prepared. However, the method of predicting solubility in organic solvents has not been established. In this study, we build the model that predict solubility in toluene using transfer learning that is suitable for predicting from small data.