Supervised machine learning for prediction of zirconocene-catalyzed α-olefin polymerization

Benjamin A. Rizkin, Ryan L. Hartman

Research output: Contribution to journalArticlepeer-review


A new approach is demonstrated in which an Artificial Neural Network (ANN) was trained with first-principles data to predict the chain length, polydispersity (Đ) and adiabatic temperature for a zirconocene-catalyzed polymerization reaction. The ANN-generated data shows good agreement with the theoretical results, with an overall R2 of 0.9987. Using its significantly enhanced computational speed, the ANN was used to analyze the reaction space, providing insights into trends seen in molecular weight and Đ with various combinations of kinetic parameters, particularly pointing out regions of desirable and undesirable operation. The network was trained in reverse and used to generate reaction rate constants from chain length and Đ, enabling a new form of kinetic deduction for polymerization reactions. This training was used to derive potential rate constants for different catalysts reported in the literature. Overall, this data indicates that ANNs are a plausible tool for analyzing data from complex metallocene-catalyzed olefin polymerizations.

Original languageEnglish (US)
Article number115224
JournalChemical Engineering Science
StatePublished - Dec 31 2019


  • Machine learning
  • Metallocene catalysis
  • Neural networks
  • Olefins
  • Polymerization
  • Process development

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering
  • Industrial and Manufacturing Engineering


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