Presentation Information
[1B02]Machine-Learning-Assisted Data Integration and Molecular Field Analysis of Norbornene Insertion in Structurally Diverse Group 4 Olefin Polymerization Catalysts
○YU ZOU1, Shigeru Yamaguchi2, Ryo Tanaka1 (1. Hiroshima University, 2. RIKEN)
Keywords:
Polymerization,Catalyst design,Machine learning
In the design of ethylene–norbornene (NB) copolymerization catalysts, promoting successive NB insertion is crucial for achieving high thermal and optical properties. Our recent studies have focused on elucidating the steric and electronic factors governing NB insertion using transition-state-based molecular field analysis. In this work, the method was applied to a broad range of group 4 olefin polymerization catalysts, including constrained-geometry catalyst (CGC)-type and imidazolin-2-iminato complexes. Analysis of the integrated data set revealed common molecular features that facilitate successive NB insertion across different catalyst classes. Guided by these insights, new group 4 complexes were designed and synthesized as candidate catalysts for ethylene–NB copolymerization. Their catalytic behavior, including activity and NB incorporation ability, is discussed in relation to the structural features identified by the molecular field analysis.