Using Computational Chemistry To Reveal Nature’s Blueprints for Single-Site Catalysis of C–H Activation

The challenge of activating inert C–H bonds motivates a study of catalysts that draws from what can be accomplished by natural enzymes and translates these advantageous features into transition-metal complex (TMC) and material mimics. Inert C–H bond activation reactivity has been observed in a diverse number of predominantly iron-containing enzymes from the heme-P450s to nonheme iron α-ketoglutarate-dependent enzymes and methane monooxygenases. Computational studies have played a key role in correlating active-site variables, such as the primary coordination sphere, oxidation state, and spin state, to reactivity. TMCs, zeolites, metal–organic frameworks (MOFs), and single-atom catalysts (SACs) are synthetic inorganic materials that have been designed to incorporate Fe active sites in analogy to single sites in enzymes. In these systems, computational studies have been essential in supporting spectroscopic assignments and quantifying the effects of the metal-local environment on C–H bond reactivity. High-throughput virtual screening tools that have been widely used for bulk metal catalysis do not readily extend to the single-site inorganic catalysts where metal–ligand bonding and localized d-electrons govern reaction energetics. These localized d-electrons can also necessitate wave function theory calculations when density functional theory (DFT) is not sufficiently accurate. Where sufficient computational or experimental data can be gathered, machine learning has helped uncover more general design rules for reactivity or stability. As we continue to investigate metalloprotein active sites, we gain insights that enable us to design stable, active, and selective single-site catalysts.