The Role of External Donors in Ziegler–Natta Catalysts through Nudged Elastic Band Simulations on Realistic-Scale Models Employing a Universal Neural Network Potential

This study undertakes a thorough computational exploration of Ziegler–Natta catalysis, emphasizing the role of external donors, particularly dicyclopentyldimethoxysilane (D donor), in the production of polypropylene. Employing the PreFerred Potential (PFP) model within the Nudged Elastic Band (NEB) method and Universal Neural Network Potentials (UNNP), we meticulously assessed the structural integrity of MgCl2 crystals, the dynamics of TiCl4 adsorption, and the kinetics of propylene insertion reactions. Our results demonstrated the precision of the PFP model in accurately replicating the crystalline structures and reaction mechanisms inherent in Ziegler–Natta catalyst systems. A pivotal finding from our research is the significant reduction in activation energy for isotactic propylene insertion, attributed to the presence of at least two D donors around a single Ti active site. Additionally, our computational approach, characterized by its speed and efficiency, successfully incorporates realistic catalyst models, encompassing a range of donor compounds, thereby bridging the gap between theoretical predictions and experimental practices. Our study not only corroborated the existing computational models but also provided novel insights into the mechanistic roles of external donors in Ziegler–Natta catalysis. The implications of these findings extend beyond theoretical studies, offering practical applications in the field of catalytic science and propylene polymerization. This research paves the way for future investigations, potentially transforming our understanding and utilization of Ziegler–Natta catalysts in industrial applications.