Constrained Geometry Catalysts in Ethylene Polymerization: From Mechanistic Understanding to High-Throughput Virtual Screening

Abstract

The rational design of constrained geometry catalysts (CGCs) for olefin polymerization remains challenging due to the intricate interplay between steric and electronic effects and the prohibitive computational cost of exploring large chemical spaces using quantum-chemical methods. In this work, we develop a mechanistically informed machine learning framework that bridges semiempirical GFN2-xTB descriptors with density functional theory (DFT)-computed activation barriers for ethylene insertion. DFT calculations identify the first insertion as the rate-determining step (RDS), enabling construction of a physically motivated surrogate model based on the cationic methyl active species. Benchmarking across eight regression algorithms identifies Gradient Boosting Regressor as the optimal predictor, delivering DFT-comparable predictive accuracy while reducing computational cost by several orders of magnitude. SHapley Additive exPlanations analysis reveals that metal electrophilicity is the primary factor governing the activation barrier of the RDS, providing direct mechanistic interpretation of the learned structure–activity relationships. Application of the model to a virtual library of approximately 1.4 × 10⁵ chemically valid CGCs via a high-throughput virtual screening workflow rapidly identifies low-barrier candidates and uncovers robust design principles, particularly the beneficial role of electron-donating cyclopentadienyl substituents. The present framework establishes an interpretable and efficient paradigm for accelerated polyolefin catalyst discovery.