RESUMEN
Zr metallocenes have significant potential to be highly tunable polyethylene catalysts through modification of the aromatic ligand framework. Here we report the development of multiple machine learning models using a large library (>700 systems) of DFT-calculated zirconocene properties and barriers for ethylene polymerization. We show that very accurate machine learning models are possible for HOMO-LUMO gaps of precatalysts but the performance significantly depends on the machine learning algorithm and type of featurization, such as fingerprints, Coulomb matrices, smooth overlap of atomic positions, or persistence images. Surprisingly, the description of the bonding hapticity, the number of direct connections between Zr and the ligand aromatic carbons, only has a moderate influence on the performance of most models. Despite robust models for HOMO-LUMO gaps, these types of machine learning models based on structure connectivity type features perform poorly in predicting ethylene migratory insertion barrier heights. Therefore, we developed several relatively robust and accurate machine learning models for barrier heights that are based on quantum-chemical descriptors (QCDs). The quantitative accuracy of these models depends on which potential energy surface structure QCDs were harvested from. This revealed a Hammett-type principle to naturally emerge showing that QCDs from the π-coordination complexes provide much better descriptions of the transition states than other potential-energy structures. Feature importance analysis of the QCDs provides several fundamental principles that influence zirconocene catalyst reactivity.
Asunto(s)
Compuestos Organometálicos , Circonio , Ligandos , Compuestos Organometálicos/química , Etilenos/química , Aprendizaje AutomáticoRESUMEN
Several renewable energy schemes aim to use the chemical bonds in abundant molecules like water and ammonia as energy reservoirs. Because the O-H and N-H bonds are quite strong (>100 kcal/mol), it is necessary to identify substances that dramatically weaken these bonds to facilitate proton-coupled electron transfer processes required for energy conversion. Usually this is accomplished through coordination-induced bond weakening by redox-active metals. However, coordination-induced bond weakening is difficult with earth's most abundant metal, aluminum, because of its redox inertness under mild conditions. Here, we report a system that uses aluminum with a redox non-innocent ligand to achieve significant levels of coordination-induced bond weakening of O-H and N-H bonds. The multisite proton-coupled electron transfer manifold described here points to redox non-innocent ligands as a design element to open coordination-induced bond weakening chemistry to more elements in the periodic table.