A Multivariate Linear Regression Approach to Predict Ethene/1-Olefin Copolymerization Statistics Promoted by Group 4 Catalysts

We report a combined multivariate linear regression (MLR) and density functional theory (DFT) approach for predicting the comonomer incorporation rate in copolymerization of ethene with 1-olefins. The MLR model was trained to correlate set 19 experimental group 4 catalysts steric electronic features dichloride catalyst precursors. Although assembled data were produced different laboratories both propene 1-hexene results considered, R2 value 0.82 leave-one-out Q2 0.72. validated against validation comprising 3 from literature not included training plus one synthesized by us. Except catalyst, predicted reasonable accuracy. Additionally, us, which 4.0%, resulted 4.5–5%. used predict 10 related zirconocenes having structural similar systems set. further explored impact precatalyst structure on analyzing 15 those These predictions DFT calculations.