Abstract
We report a combined multivariate linear regression (MLR) and density functional theory (DFT) approach for predicting the comonomer incorporation rate in the copolymerization of ethene with 1-olefins. The MLR model was trained to correlate the incorporation rate of a set of 19 experimental group 4 catalysts to steric and electronic features of the dichloride catalyst precursors. Although the assembled experimental data were produced in different laboratories and both propene and 1-hexene copolymerization results were considered, the trained MLR model results in a R2 value of 0.82 and a leave-one-out Q2 value of 0.72. The trained model was validated against a validation set comprising 3 catalysts from the literature and not included in the training set plus one catalyst synthesized by us. Except for one literature catalyst, data in the validation set were predicted with reasonable accuracy. Additionally, a catalyst synthesized by us, for which the MLR model predicted a comonomer incorporation of 4.0%, resulted in a 1-hexene experimental incorporation of 4.5–5%. The trained MLR model was used to predict the comonomer incorporation rate of 10 related zirconocenes having structural features similar to the 19 systems in the training set. We further explored the impact of the precatalyst structure on the comonomer incorporation rate by analyzing a set of 15 zirconocenes having steric and electronic features different from those in the training set. These predictions were validated by DFT calculations.
Highlights
Polyethylene (PE) is a highly versatile polymeric material, with a worldwide production approaching 108 tons/year and a market spanning from commodities to high-value products.[1−3] This range of applications is possible because of the range of properties that different PEs have
23 on the Curtin−Hammett principle, largely used to model several aspects of olefin polymerization, which assumes that the geometries for each complex, by performing a relative rotation experimental behavior is determined by the energy difference of the two Cp rings by 0, 120, and 240°
We have described the development of a synergic multivariate linear regression (MLR)/density functional theory (DFT) model for the prediction of the ethene/1-olefin copolymerization tendency shown by group 4 catalysts
Summary
Polyethylene (PE) is a highly versatile polymeric material, with a worldwide production approaching 108 tons/year and a market spanning from commodities to high-value products.[1−3] This range of applications is possible because of the range of properties that different PEs have. Insertion polymerization, especially by group 4 homogeneous catalysts and the industrially used heterogeneous Ziegler−Natta catalysts,[1,2] results in an almost perfectly linear macromolecule with no branching, leading to highdensity PE (HDPE) Between these two limiting cases, there is the domain of PE with macromolecules presenting a small number of randomly distributed branches of fixed length. There is great industrial interest in achieving ethene/1-olefin copolymers with the desired amount of the comonomer since tuning the type and amount of the inserted comonomer allows varying many
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