[7p-N403-1~9] 13.2 Exploratory Materials, Physical Properties, Devices

3:45 PM - 4:00 PM JST(6:45 AM - 7:00 AM UTC)Presentation by Applicant for JSAP Young Scientists Presentation Award

[7p-N403-9] Prediction of Solvent Properties by Machine Learning with COSMO-RS Descriptors

〇Itsuki Kawanami¹, Hiroto Yokoyama¹, Akiko Kumada¹, Masahiro Sato¹ (1.Univ. of Tokyo Engineering)

Keywords:

semiconductor,materials,material property

In this study, we incorporated thermodynamic property values obtained from COSMOtherm, based on COSMO-RS theory, as descriptors in machine learning models to improve the prediction accuracy of solvent properties such as surface tension, solubility, and boiling point. Compared to conventional methods using quantum chemical descriptors, our approach demonstrated higher accuracy and superior extrapolation performance for unknown solvents, highlighting its potential as a promising tool for novel solvent discovery.