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Two solution reactors in series were utilized to synthesize comb‐branched high‐density polyethylene (HDPE), cbHDPE, where the first reactor prepares vinyl‐terminated HDPE macromers catalyzed by an organometallic catalyst favoring beta hydride transfer and the second reactor copolymerizes HDPE macromers with ethylene using a different organometallic catalyst capable of incorporating macromers. A bimodal HDPE, biHDPE with bimodalities in molecular weight, and hexene content of the desired composition distribution was also prepared in a gas phase reactor using silica supported dual organometallic catalysts. By blending 3% solution‐made cbHDPE into the gas‐phase biHDPE, the resulting trimodal HDPE preserves the excellent stiffness and toughness of the bimodal HDPE while having exceptional melt strength and processability. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018, 135, 45755.

AFM phase images were collected on impact copolymer samples that differ in bulk mechanical properties and microstructure. A deep learning model based on a convolutional neural net (CNN) successfully classified some combinations of ICP's based on microstructure. A separate regression-based CNN correlated the AFM phase images with various bulk mechanical properties, showing good results with yield strength and ultimate elongation percentage and weak results with flexural modulus and notched izod. The results observed from the deep learning model reveal a relationship between the microstructures as captured by the AFM phase images with the different bulk material properties.

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