Development of Pd-immobilized porous polymer catalysts via Bayesian optimization
摘要
In this study, a Pd-polymeric porous immobilized catalyst is prepared for the Suzuki–Miyaura coupling reactions by employing a Bayesian optimization method to optimize the catalyst. This research represents the first endeavor to utilize machine learning for the optimization of polymer-immobilized catalysts and provides a novel perspective on utilizing machine learning for the optimization of complex materials.