A Multivariate Linear Regression Approach to Predict Ethene/1-Olefin Copolymerization Statistics Promoted by Group 4 Catalysts
KAUST DepartmentBiological and Environmental Science and Engineering (BESE) Division
Chemical Science Program
KAUST Catalysis Center (KCC)
Physical Science and Engineering (PSE) Division
Online Publication Date2021-03-17
Print Publication Date2021-04-02
Embargo End Date2022-03-17
Permanent link to this recordhttp://hdl.handle.net/10754/668168
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AbstractWe 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.
CitationMaity, B., Cao, Z., Kumawat, J., Gupta, V., & Cavallo, L. (2021). A Multivariate Linear Regression Approach to Predict Ethene/1-Olefin Copolymerization Statistics Promoted by Group 4 Catalysts. ACS Catalysis, 4061–4070. doi:10.1021/acscatal.0c04856
SponsorsL.C. acknowledges the King Abdullah University of Science and Technology (KAUST) for support and the KAUST Supercomputing Laboratory for providing computational resources of the supercomputer Shaheen II.
PublisherAmerican Chemical Society (ACS)
Except where otherwise noted, this item's license is described as This document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Catalysis, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://pubs.acs.org/doi/10.1021/acscatal.0c04856.