Structural characterization of a non-heme iron active site in zeolites that hydroxylates methane

Snyder, B.; Böttger, Lars H.; Bols, Max L.; Yan, James J.; Rhoda, Hannah M.; Jacobs, Ariel B.; Hu, Michael Y.; Zhao, Jiyong; Ercan, E.; Hedman, Britt; Hodgson, Keith O.; Schoonheydt, Robert A.; Sels, Bert F.; Solomon, Edward I.

doi:10.1073/pnas.1721717115

Cited by 76 publications

(121 citation statements)

References 31 publications

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“…Conversely, it is known that enzymes readily catalyze selective partial oxidation through the formation of high-valent metal−oxo (e.g., Fe(IV)O) species. 9−12 These biological catalysts have motivated the development of bioinspired molecular 13−17 and extended 18 (e.g., zeolite 19 or metal−organic framework 20 ) catalysts for C−H activation.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation

et al. 2019

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Metal−oxo moieties are important catalytic intermediates in the selective partial oxidation of hydrocarbons and in water splitting. Stable metal−oxo species have reactive properties that vary depending on the spin state of the metal, complicating the development of structure−property relationships. To overcome these challenges, we train machine-learning (ML) models capable of predicting metal−oxo formation energies across a range of first-row metals, oxidation states, and spin states. Using connectivity-only features tailored for inorganic chemistry as inputs to kernel ridge regression or artificial neural network (ANN) ML models, we achieve good mean absolute errors (4−5 kcal/mol) on set-aside test data across a range of ligand orientations. Analysis of feature importance for oxo formation energy prediction reveals the dominance of nonlocal, electronic ligand properties in contrast to other transition metal complex properties (e.g., spin-state or ionization potential). We enumerate the theoretical catalyst space with an ANN, revealing expected trends in oxo formation energetics, such as destabilization of the metal−oxo species with increasing d-filling, as well as exceptions, such as weak correlations with indicators of oxidative stability of the metal in the resting state or unexpected spin-state dependence in reactivity. We carry out uncertainty-aware evolutionary optimization using the ANN to explore a >37 000 candidate catalyst space. New metal and oxidation state combinations are uncovered and validated with density functional theory (DFT), including counterintuitive oxo formation energies for oxidatively stable complexes. This approach doubles the density of confirmed DFT leads in originally sparsely populated regions of property space, highlighting the potential of ML-model-driven discovery to uncover catalyst design rules and exceptions.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation

et al. 2019

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“…Overall, these studies complement additional investigations of copper-mediated and catalyzed methane oxidation chemistry within other porous materials. [48][49][50][51] Unlike pMMO, where the nuclearity of the active site is still debated, the hydroxylase subunit of soluble methane monooxygenase (sMMO) is generally agreed to feature a dimeric Fe active site. 40 The compatibility of MIL-53(Al, Fe) with H 2 O 2 underscores two important distinctions between biomimetic MOFs and the enzymes they emulate.…”

Section: Methane and Ethanementioning

confidence: 99%

Bioinspired chemistry at MOF secondary building units

Bour

Wright

et al. 2020

Chem. Sci.

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“…Recently, Solomon and coworkers (8) characterized a potent zeolite-supported ferryl (FeO 2+ ) species capable of hydroxylating methane at room temperature. Now, in PNAS, Synder et al (9) extend characterization of this species, using synchrotronbased spectroscopies to access valuable bonding parameters essential to defining the electronic structure underpinning the reactivity of this ferryl.…”

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confidence: 99%

“…The structural characterization of α-Fe and α-O reported in PNAS (9) offers a rationale for the oxidative potency. Using Fe K-edge X-ray absorption spectroscopy and extended X-ray absorption fine structure (EXAFS) data obtained at the Stanford Synchrotron Radiation Lightsource, Synder et al (9) established the inner-sphere metrical parameters of α-Fe: four innersphere zeolite O ligands at 2.02 ± 0.03 Å. For α-O, a 1.63 ± 0.03 Å Fe=O scatterer completes the inner coordination sphere.…”

mentioning

confidence: 99%