A Density Functional Study on Ethylene Trimerization and Tetramerization Using Real Sasol Cr-PNP Catalysts

To gain molecular-level insight into the intricate features of the catalytic behavior of chromium–diphosphine complexes regarding ethylene tri- and tetramerizations, we performed density functional theory (DFT) calculations. The selective formation of 1-hexene and 1-octene by the tri- and tetramerizations of ethylene are generally accepted to follow the metallacycle mechanism. To explore the mechanism of ethylene tri- and tetramerizations, we used a real Sasol chromium complex with a nitrogen-bridged diphosphine ligand with ortho- and para-methoxyaryl substituents. We explore the trimerization mechanism for ethylene first and, later on for comparison, we extend the potential energy surfaces (PES) for the tetramerization of ethylene with both catalysts. The calculated results reveal that the formation of 1-hexene and 1-octene with the ortho-methoxyaryl and para-methoxyaryl Cr-PNP catalysts have nearly similar potential energy surfaces (PES). From the calculated results important insights are gained into the tri- and tetramerizations. The tetramerization of ethylene with the para-methoxyaryl Cr-PNP catalyst lowers the barrier height by ~2.6 kcal/mol compared to that of ethylene with the ortho-methoxyaryl Cr-PNP catalyst. The selectivity toward trimerization or tetramerization comes from whether the energy barrier for ethylene insertion to metallacycloheptane is higher than β-hydride transfer to make 1-hexene. The metallacycle mechanism with Cr (I)–Cr (III) intermediates is found to be the most favored, with the oxidative coupling of the two coordinated ethylenes to form chromacyclopentane being the rate-determining step.

Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization

The use of data science tools to provide the emergence of non-trivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here, we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity. This involved the calculation of 1-hexene : 1-octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr-N distance, Cr-α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give >95% selectivity for 1-octene.