2026-10-11T15:10:34Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33951957 2026-09-21T10:31:01Z category_1 category_4 category_12 category_45 category_734 category_931 category_811 portal_63 item_type_3 month_year_09_2026

Constrained Geometry Catalysts in Ethylene Polymerization: From Mechanistic Understanding to High-Throughput Virtual Screening Shi-Yun Feng (25082949) Rui-Hong He (24467235) Xu-Bin Wang (25082952) Qi Yang (108283) Jing-Shuang Dang (1863709) Biophysics Biochemistry Cell Biology Sociology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified reducing computational cost prohibitive computational cost primary factor governing present framework establishes donating cyclopentadienyl substituents density functional theory constrained geometry catalysts comparable predictive accuracy bridges semiempirical gfn2 4 × 10 throughput virtual screening dft calculations identify 5 </ sup virtual library delivering dft xtb descriptors several orders rational design optimal predictor metal electrophilicity mechanistic understanding intricate interplay first insertion ethylene polymerization ethylene insertion electronic effects efficient paradigm determining step chemical methods beneficial role barrier candidates approximately 1 activation barrier 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. 2026-09-21T00:00:00Z Dataset Dataset 10.1021/acs.organomet.6c00254.s003 https://figshare.com/articles/dataset/Constrained_Geometry_Catalysts_in_Ethylene_Polymerization_From_Mechanistic_Understanding_to_High-Throughput_Virtual_Screening/33951957 CC BY-NC 4.0