Machine Learning Prediction of Copolymerization of Ethylene and Methyl Acrylate by Palladium Complexes Bearing Phosphine–Sulfonate Ligands

Abstract

Various types of palladium complexes bearing phosphine-sulfonate ligands have been developed for the coordination–insertion copolymerization of olefins with polar monomers. Characteristic features of the ligands, such as electronic and steric properties were discussed in relation to their catalytic performance. Aiming at further analysis of the obtained data, here we report development of prediction method for copolymerization of ethylene and methyl acrylate using machine learning. As a result of prediction by machine learning, parameters that are important for molecular weight of the obtained polymers and polymerization activity were obtained. These results suggest concepts for new catalyst designs.