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.