Computer-assisted design of CGC catalysts for ethylene/1-octene copolymerization: A combined DFT and artificial neural network approach

Abstract

High temperature solution polymerization of ethylene/1-octene is the main means to produce polyolefin elastomer (POE), one of the most important polyolefin materials, and the metallocene catalysts with high-efficiency are the key to achieve POE production. However, there remains a deficiency in a universal approach for creating catalysts for ethylene/1-octene copolymerization. In this document, we outlined a method for designing catalysts for ethylene/1-octene copolymerization by combining density functional theory (DFT) with an artificial neural network (ANN) was proposed. By optimizing the structure and computing electronically a sequence of constrained geometry catalysts (CGC) by DFT, descriptors related to steric and electronic descriptors were gathered to create the dataset. After ANN training, an ANN-based model using for predicting catalyst activity with high R2 value was optimized, and a class of novel highly efficient CGC have been successfully designed. Furthermore, an exploration was conducted into how the structure of catalysts correlates with their performance. The electronic properties of the catalysts were found to have more significantly effect on the catalytic performance than the steric properties. In addition, the effect of the β-site substitution of the cyclopentadienyl group on the catalytic activity of predicted CGC was studied and discussed. The groups with ring-structure, especially with conjugate system, were proved to possess a good promoting effect on the catalytic activity.