End-to-End High-Throughput Approach for Data-Driven Internal Donor Development in Heterogeneous Ziegler-Natta Propylene Polymerization

Internal donors (IDs) play a decisive role in shaping the structure and performance of Ziegler-Natta catalyst formulations for the isotactic polypropylene production. Unfortunately, their diverse and intricate functions remain elusive, and rational ID discovery therefore is still problematic. Exploitation of artificial intelligence methods such as machine learning, in turn, has been hindered by the lack of training datasets with adequate quality and size. This study proposes an integrated high-throughput workflow encompassing catalyst synthesis, propylene polymerization, and polypropylene characterization. Its application to an ID library of 35 molecules generated a robust and consistent dataset, which highlighted important and intriguing quantitative structure-properties relations (QSPRs). Furthermore, by fingerprinting ID molecular structure in combination with feature selection, a black-box QSPR model correlating ID molecular structure and catalytic performance was successfully implemented. This study demonstrates that the combination of high-throughput experimentation and machine learning is a promising asset for accelerating the research and development of Ziegler-Natta catalysts.