From Mechanistic Investigation to Quantitative Prediction: Kinetics of Homogeneous Transition Metal-Catalyzed α-Olefin Polymerization Predicted by Computational Chemistry This chapter critically analyzes the capabilities of density functional theory for prediction of key catalyst performance indicators in homogenous transition metal-catalyzed α-olefin polymerization. Several indicators (e.g., regioselectivity in propene homopolymerization, dormancy, and comonomer affinities in copolymerization) can be reliably predicted, often to a greater accuracy than ±1 kcal/mol (“chemical accuracy”) due to fortuitous error cancellation. Several other performance indicators, e.g., stereoselectivity and molecular weight capability, can be problematic in certain cases. Several phenomena, e.g., chain transfer to main group metal, β-hydrogen transfer to metal, back-skip insertion/chain epimerization or homolysis pose challenges. The state-of-the-art in predicting catalyst performance indicators is discussed, along with potential pitfalls and cures, which is important for computational catalyst prescreening.

From Mechanistic Investigation to Quantitative Prediction

Ehm, Christian;Zaccaria, Francesco;Cipullo, Roberta
2019

Abstract

From Mechanistic Investigation to Quantitative Prediction: Kinetics of Homogeneous Transition Metal-Catalyzed α-Olefin Polymerization Predicted by Computational Chemistry This chapter critically analyzes the capabilities of density functional theory for prediction of key catalyst performance indicators in homogenous transition metal-catalyzed α-olefin polymerization. Several indicators (e.g., regioselectivity in propene homopolymerization, dormancy, and comonomer affinities in copolymerization) can be reliably predicted, often to a greater accuracy than ±1 kcal/mol (“chemical accuracy”) due to fortuitous error cancellation. Several other performance indicators, e.g., stereoselectivity and molecular weight capability, can be problematic in certain cases. Several phenomena, e.g., chain transfer to main group metal, β-hydrogen transfer to metal, back-skip insertion/chain epimerization or homolysis pose challenges. The state-of-the-art in predicting catalyst performance indicators is discussed, along with potential pitfalls and cures, which is important for computational catalyst prescreening.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11588/738526
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