Structure–Activity Relationships That Identify Metal–Organic Framework Catalysts for Methane Activation

Exploring the Impact of Active Site Structure on the Conversion of Methane to Methanol in Cu-Exchanged Zeolites

In the past, Cu-oxo or -hydroxy clusters hosted in zeolites have been suggested to enable the selective conversion of methane to methanol, but the impact of the active site's stoichiometry and structure on methanol production is still poorly understood. Herein, we apply theoretical modeling in conjunction with experiments to study the impact of these two factors on partial methane oxidation in the Cu-exchanged zeolite SSZ-13. Phase diagrams developed from first-principles suggest that Cu-hydroxy or Cu-oxo dimers are stabilized when O2 or N2O are used to activate the catalyst, respectively. We confirm these predictions experimentally and determine that in a stepwise conversion process, Cu-oxo dimers can convert twice as much methane to methanol compared to Cu-hydroxyl dimers. Our theoretical models rationalize how Cu-di-oxo dimers can convert up to two methane molecules to methanol, while Cu-di-hydroxyl dimers can convert only one methane molecule to methanol per catalytic cycle. These findings imply that in Cu clusters, at least one oxo group or two hydroxyl groups are needed to convert one methane molecule to methanol per cycle. This simple structure-activity relationship allows to intuitively understand the potential of small oxygenated or hydroxylated transition metal clusters to convert methane to methanol.

A US perspective on closing the carbon cycle to defossilize difficult-to-electrify segments of our economy

Electrification to reduce or eliminate greenhouse gas emissions is essential to mitigate climate change. However, a substantial portion of our manufacturing and transportation infrastructure will be difficult to electrify and/or will continue to use carbon as a key component, including areas in aviation, heavy-duty and marine transportation, and the chemical industry. In this Roadmap, we explore how multidisciplinary approaches will enable us to close the carbon cycle and create a circular economy by defossilizing these difficult-to-electrify areas and those that will continue to need carbon. We discuss two approaches for this: developing carbon alternatives and improving our ability to reuse carbon, enabled by separations. Furthermore, we posit that co-design and use-driven fundamental science are essential to reach aggressive greenhouse gas reduction targets.

Insights into hydrogen and methane storage capacities: Grand canonical Monte Carlo simulations of SIGSUA

In the pursuit of sustainable energy solutions, the development of materials with efficient hydrogen and methane storage capacities is imperative, particularly for advancing hydrogen-powered vehicles. Metal-organic frameworks (MOFs) have emerged as promising candidates to meet the stringent targets set by the Department of Energy for both hydrogen and methane storage. This study employs Grand Canonical Monte Carlo simulations to investigate the usable hydrogen and methane gravimetric and volumetric storage capacities of the recently synthesized SIGSUA. A comparative analysis encompasses the selected MOFs with similar metal compositions, those with comparable density and average pore radius, and classical benchmarks, such as IRMOF-15 and IRMOF-20, all evaluated at room temperature and moderate pressures ranging from 25 to 35 MPa. The results reveal that SIGSUA demonstrates noteworthy gravimetric and volumetric storage capacities for both hydrogen and methane, rivaling or surpassing those of the selected MOFs for analysis. These findings underscore the potential of SIGSUA in advancing clean energy storage technologies.

Improving gas adsorption modeling for MOFs by local calibration of Hubbard U parameters

While computational screening with density functional theory (DFT) is frequently employed for the screening of metal-organic frameworks (MOFs) for gas separation and storage, commonly applied generalized gradient approximations (GGAs) exhibit self-interaction errors, which hinder the predictions of adsorption energies. We investigate the Hubbard U parameter to augment DFT calculations for full periodic MOFs, targeting a more precise modeling of gas molecule-MOF interactions, specifically for N2, CO2, and O2. We introduce a calibration scheme for the U parameter, which is tailored for each MOF, by leveraging higher-level calculations on the secondary building unit (SBU) of the MOF. When applied to the full periodic MOF, the U parameter calibrated against hybrid HSE06 calculations of SBUs successfully reproduces hybrid-quality calculations of the adsorption energy of the periodic MOF. The mean absolute deviation of adsorption energies reduces from 0.13 eV for a standard GGA treatment to 0.06 eV with the calibrated U, demonstrating the utility of the calibration procedure when applied to the full MOF structure. Furthermore, attempting to use coupled cluster singles and doubles with perturbative triples calculations of isolated SBUs for this calibration procedure shows varying degrees of success in predicting the experimental heat of adsorption. It improves accuracy for N2 adsorption for cases of overbinding, whereas its impact on CO2 is minimal, and ambiguities in spin state assignment hinder consistent improvements of O2 adsorption. Our findings emphasize the limitations of cluster models and advocate the use of full periodic MOF systems with a calibrated U parameter, providing a more comprehensive understanding of gas adsorption in MOFs.

Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training

Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu]2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu]2+-catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and, subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step toward overcoming the long-standing barriers within the computational heterogeneous catalysis community.

Low-Valent Metals in Metal-Organic Frameworks Via Post-Synthetic Modification

Metal-organic frameworks (MOFs) provide uniquely tunable, periodic platforms for site-isolation of reactive low-valent metal complexes of relevance in modern catalysis, adsorptive applications, and fundamental structural studies. Strategies for integrating such species in MOFs include post-synthetic metalation, encapsulation and direct synthesis using low-valent organometallic complexes as building blocks. These approaches have each proven effective in enhancing catalytic activity, modulating product distributions (i.e., by improving catalytic selectivity), and providing valuable mechanistic insights. In this minireview, we explore these different strategies, as applied to isolate low-valent species within MOFs, with a particular focus on examples that leverage the unique crystallinity, permanent porosity and chemical mutability of MOFs to achieve deep structural insights that lead to new paradigms in the field of hybrid catalysis.

Fine-Tuning Dual Single-Atom Metal Sites on Graphene toward Enhanced Oxygen Reduction Reaction Activity

The oxygen reduction reaction (ORR) remains at the forefront of research in diverse energy and sustainability domains. While graphene-supported single-atom catalysts (SACs) have garnered attention for optimizing ORR efficiency, tailoring the interactions between adjacent single-atom sites presents intricate challenges. In this study, we leveraged density functional theory (DFT) calculations and cutting-edge machine learning (ML) techniques to explore 144 graphene-supported SACs, featuring interacting M1-N4 and M2-N4 moieties (M1, M2 = Mn, Fe, Co, Ni, Cu, Ru, Rh, Pd, Ag Ir, Pt, Au), denoted as M1-M2. By tailoring these interactions, we discovered 13 exceptional SACs outperforming the benchmark catalyst Fe(OH)-N4, including the best-performing Fe-Pd and several non-noble-metal SACs like Fe-Ag, Ag-Cu, and Ag-Ag. Venturing further, our ML models effectively capture the correlation between single-atom metal properties and overpotential, offering tools for rational electrocatalyst design. Our study illuminates the path to efficient SAC-catalyzed ORR, fostering a sustainable, energy-efficient future.

MOF-Based Chemiresistive Gas Sensors: Toward New Functionalities

The sensing performances of gas sensors must be improved and diversified to enhance quality of life by ensuring health, safety, and convenience. Metal-organic frameworks (MOFs), which exhibit an extremely high surface area, abundant porosity, and unique surface chemistry, provide a promising framework for facilitating gas-sensor innovations. Enhanced understanding of conduction mechanisms of MOFs has facilitated their use as gas-sensing materials, and various types of MOFs have been developed by examining the compositional and morphological dependences and implementing catalyst incorporation and light activation. Owing to their inherent separation and absorption properties and catalytic activity, MOFs are applied as molecular sieves, absorptive filtering layers, and heterogeneous catalysts. In addition, oxide- or carbon-based sensing materials with complex structures or catalytic composites can be derived by the appropriate post-treatment of MOFs. This review discusses the effective techniques to design optimal MOFs, in terms of computational screening and synthesis methods. Moreover, the mechanisms through which the distinctive functionalities of MOFs as sensing materials, heterostructures, and derivatives can be incorporated in gas-sensor applications are presented.

Computational Discovery of Stable Metal-Organic Frameworks for Methane-to-Methanol Catalysis

The challenge of direct partial oxidation of methane to methanol has motivated the targeted search of metal-organic frameworks (MOFs) as a promising class of materials for this transformation because of their site-isolated metals with tunable ligand environments. Thousands of MOFs have been synthesized, yet relatively few have been screened for their promise in methane conversion. We developed a high-throughput virtual screening workflow that identifies MOFs from a diverse space of experimental MOFs that have not been studied for catalysis, yet are thermally stable, synthesizable, and have promising unsaturated metal sites for C-H activation via a terminal metal-oxo species. We carried out density functional theory calculations of the radical rebound mechanism for methane-to-methanol conversion on models of the secondary building units (SBUs) from 87 selected MOFs. While we showed that oxo formation favorability decreases with increasing 3d filling, consistent with prior work, previously observed scaling relations between oxo formation and hydrogen atom transfer (HAT) are disrupted by the greater diversity in our MOF set. Accordingly, we focused on Mn MOFs, which favor oxo intermediates without disfavoring HAT or leading to high methanol release energies─a key feature for methane hydroxylation activity. We identified three Mn MOFs comprising unsaturated Mn centers bound to weak-field carboxylate ligands in planar or bent geometries with promising methane-to-methanol kinetics and thermodynamics. The energetic spans of these MOFs are indicative of promising turnover frequencies for methane to methanol that warrant further experimental catalytic studies.

Insight into the direct conversion of methane to methanol on modified ZIF-204 from the perspective of DFT-based calculations

Direct oxidation of methane over oxo-doped ZIF-204, a bio-mimetic metal-organic framework, is investigated under first-principles calculations based on density functional theory. In the pristine ZIF-204, the tetrahedral methane molecule anchors to an open monocopper site via the so-called η² configuration with a physisorption energy of 0.24 eV. This weak binding arises from an electrostatic interaction between the negative charge of carbon in the methane molecule and the positive Cu²⁺ cation in the framework. In the modified ZIF-204, the doped oxo species is stabilized at the axial position of a CuN₄-base square pyramid at a distance of 2.06 Å. The dative covalent bond between Cu and oxo is responsible for the formation energy of 1.06 eV. With the presence of the oxo group, the presenting of electrons in the O_p_z orbital accounts for the adsorption of methane via hydrogen bonding with an adsorption energy of 0.30 eV. The methane oxidation can occur via either a concerted direct oxo insertion mechanism or a hydrogen-atom abstraction radical rebound mechanism. Calculations on transition-state barriers show that reactions via the concerted direct oxo insertion mechanism can happen without energy barriers. Concerning the hydrogen-atom abstraction radical rebound mechanism, the C-H bond dissociation of the CH₄ molecule is barrierless, but the C-O bond recombination to form the CH₃OH molecule occurs through a low barrier of 0.16 eV. These predictions suggest the modified ZIF-204 is a promising catalyst for methane oxidization.

Selective Methane Oxidation to Acetic Acid Using Molecular Oxygen over a Mono-Copper Hydroxyl Catalyst

Acetic acid is an industrially important chemical, produced mainly via carbonylation of methanol using precious metal-based homogeneous catalysts. As a low-cost feedstock, methane is commercially transformed to acetic acid via a multistep process involving energy-intensive methane steam reforming, methanol synthesis, and, subsequently, methanol carbonylation. Here, we report a direct single-step conversion of methane to acetic acid using molecular oxygen (O₂) as the oxidant under mild conditions over a mono-copper hydroxyl site confined in a porous cerium metal-organic framework (MOF), Ce-UiO-Cu(OH). The Ce-UiO MOF-supported single-site copper hydroxyl catalyst gave exceptionally high acetic acid productivity of 335 mmolg_cat^-1 in 96% selectivity with a Cu TON up to 400 at 115 °C in water. Our spectroscopic and theoretical studies and controlled experiments reveal that the conversion of methane to acetic acid occurs via oxidative carbonylation, where methane is first activated at the copper hydroxyl site via σ-bond metathesis to afford Cu-methyl species, followed by carbonylation with in situ-generated carbon monoxide and subsequent hydrolysis by water. This work may guide the rational design of heterogeneous abundant metal catalysts for the activation and conversion of methane to acetic acid and other valuable chemicals under mild and environmentally friendly reaction conditions.

Methane Activation and Coupling Pathways on Ni2P Catalyst

The direct catalytic conversion of methane (CH4) to higher hydrocarbons has attracted considerable attention in recent years because of the increasing supply of natural gas. Efficient and selective catalytic conversion of methane to value-added products, however, remains a major challenge. Recent studies have shown that the incorporation of phosphorus atoms in transition metals improves their selectivity and resistance to coke formation for many catalytic reactions. In this work, we report a density function theory-based investigation of methane activation and C2 product formation on Ni2P(001). Our results indicate that, despite the lower reactivity of Ni2P relative to Ni, the addition of phosphorus atoms hinders excessive dehydrogenation of methane to CH* and C* species, thus reducing carbon deposition on the surface. CH3* and CH2* moieties, instead, are more likely to be the most abundant surface intermediates once the initial C–H bond in methane is activated with a barrier of 246 kJ mol−1. The formation of ethylene from 2CH2* on Ni2P is facile with a barrier of 56 kJ mol−1, which is consistent with prior experimental studies. Collectively, these findings suggest that Ni2P may be an attractive catalyst for selective methane conversion to ethylene.

Modulating the Energetics of C-H Bond Activation in Methane by Utilizing Metalated Porphyrinic Metal-Organic Frameworks

In recent years, much effort has been directed toward utilizing metal-organic frameworks (MOFs) for activating C-H bonds of light alkanes. The energy demanding steps involved in the catalytic pathway are the formation of metal-oxo species and the subsequent cleavage of the C-H bonds of alkanes. With the intention of exploring the tunability of the activation barriers involved in the catalytic pathway of methane hydroxylation, we have employed density functional theory to model metalated porphyrinic MOFs (MOF-525(M)). We find that the heavier congeners down a particular group have high exothermic oxo-formation enthalpies ΔH_O and hence are associated with low N₂O activation barriers. Independent analyses of activation barriers and structure-activity relationship leads to the conclusion that MOF-525(Ru) and MOF-525(Ir) can act as an effective catalysts for methane hydroxylation. Hence, ΔH_O has been found to act as a guide, in the first place, in choosing the optimum catalyst for methane hydroxylation from a large set of available systems.

Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis

Virtual high-throughput screening (VHTS) and machine learning (ML) have greatly accelerated the design of single-site transition-metal catalysts. VHTS of catalysts, however, is often accompanied with a high calculation failure rate and wasted computational resources due to the difficulty of simultaneously converging all mechanistically relevant reactive intermediates to expected geometries and electronic states. We demonstrate a dynamic classifier approach, i.e., a convolutional neural network that monitors geometry optimizations on the fly, and exploit its good performance and transferability in identifying geometry optimization failures for catalyst design. We show that the dynamic classifier performs well on all reactive intermediates in the representative catalytic cycle of the radical rebound mechanism for the conversion of methane to methanol despite being trained on only one reactive intermediate. The dynamic classifier also generalizes to chemically distinct intermediates and metal centers absent from the training data without loss of accuracy or model confidence. We rationalize this superior model transferability as arising from the use of electronic structure and geometric information generated on-the-fly from density functional theory calculations and the convolutional layer in the dynamic classifier. When used in combination with uncertainty quantification, the dynamic classifier saves more than half of the computational resources that would have been wasted on unsuccessful calculations for all reactive intermediates being considered.

New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts

Despite decades of effort, no earth-abundant homogeneous catalysts have been discovered that can selectively oxidize methane to methanol. We exploit active learning to simultaneously optimize methane activation and methanol release calculated with machine learning-accelerated density functional theory in a space of 16 M candidate catalysts including novel macrocycles. By constructing macrocycles from fragments inspired by synthesized compounds, we ensure synthetic realism in our computational search. Our large-scale search reveals that low-spin Fe(II) compounds paired with strong-field (e.g., P or S-coordinating) ligands have among the best energetic tradeoffs between hydrogen atom transfer (HAT) and methanol release. This observation contrasts with prior efforts that have focused on high-spin Fe(II) with weak-field ligands. By decoupling equatorial and axial ligand effects, we determine that negatively charged axial ligands are critical for more rapid release of methanol and that higher-valency metals [i.e., M(III) vs M(II)] are likely to be rate-limited by slow methanol release. With full characterization of barrier heights, we confirm that optimizing for HAT does not lead to large oxo formation barriers. Energetic span analysis reveals designs for an intermediate-spin Mn(II) catalyst and a low-spin Fe(II) catalyst that are predicted to have good turnover frequencies. Our active learning approach to optimize two distinct reaction energies with efficient global optimization is expected to be beneficial for the search of large catalyst spaces where no prior designs have been identified and where linear scaling relationships between reaction energies or barriers may be limited or unknown.

FAIR data enabling new horizons for materials research

The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science.

Exploring mechanistic routes for light alkane oxidation with an iron-triazolate metal-organic framework

In this work, we computationally explore the formation and subsequent reactivity of various iron-oxo species in the iron-triazolate framework Fe₂(μ-OH)₂(bbta) (H₂bbta = 1H,5H-benzo(1,2-d:4,5-d')bistriazole) for the catalytic activation of strong C-H bonds. With the direct conversion of methane to methanol as the probe reaction of interest, we use density functional theory (DFT) calculations to evaluate multiple mechanistic pathways in the presence of either N₂O or H₂O₂ oxidants. These calculations reveal that a wide range of transition metal-oxo sites - both terminal and bridging - are plausible in this family of metal-organic frameworks, making it a unique platform for comparing the electronic structure and reactivity of different proposed active site motifs. Based on the DFT calculations, we predict that Fe₂(μ-OH)₂(bbta) would exhibit a relatively low barrier for N₂O activation and energetically favorable formation of an [Fe(O)]²⁺ species that is capable of oxidizing C-H bonds. In contrast, the use of H₂O₂ as the oxidant is predicted to yield an assortment of bridging iron-oxo sites that are less reactive. We also find that abstracting oxo ligands can exhibit a complex mixture of both positive and negative spin density, which may have broader implications for relating the degree of radical character to catalytic activity. In general, we consider the coordinatively unsaturated iron sites to be promising for oxidation catalysis, and we provide several recommendations on how to further tune the catalytic properties of this family of metal-triazolate frameworks.

MOFSimplify, machine learning models with extracted stability data of three thousand metal-organic frameworks

We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal–organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.

Measurement(s)	thermal decomposition
Technology Type(s)	thermogravimetry

Light alkane oxidation over well-defined active sites in metal–organic framework materials

We review structure–catalytic property relationships for MOF materials used in the direct oxidation of light alkanes, focusing specifically on the elucidation of active site structures and probes for reaction mechanisms.

Catalytic properties of the ferryl ion in the solid state: a computational review

This review summarises the last findings in the emerging field of heterogeneous catalytic oxidation of light alkanes by ferryl species supported on solid-state systems such as the conversion of methane into methanol by FeO-MOF74.

Lowering the C–H bond activation barrier of methane by means of SAC@Cu(111): periodic DFT investigations

Methane has long been in the world's spotlight as the simplest yet one of the most notorious hydrocarbons; here, we study the efficiency of single-atom catalysts (SACs) for methane activation using density functional theory (DFT).

Correlation between the Metal and Organic Components, Structure Property, and Gas-Adsorption Capacity of Metal-Organic Frameworks

Metal-organic frameworks (MOFs) are materials in which metals and organic compounds form crystalline and porous structures. Previous studies have investigated the relationships between the structure properties and physical properties of MOFs through molecular simulations, but the overall relationships in MOFs, including the relationships between the metals and organic components and the experimentally measured physical properties, have not been clarified. In this study, we developed two regression models between three elements in MOFs: the components, structure properties, and gas-adsorption capacities as physical properties. Using a nonlinear regression analysis method, we succeeded in predicting the structure properties from the components and the physical properties from the structure properties.

Methane activation on dual-atom catalysts supported on graphene

Dual-atom Fe catalysts supported by three nitrogen atom doped graphene (Fe-TM/GP, where TM = Ti, V, Cr, Mn, Fe, Co, Ni, and Cu) are explored for methane adsorption and activation. The addition of the second metal significantly tunes the properties of the catalysts. The main factor influencing methane adsorption is electron transfer. The second metal promotes methane adsorption by altering the electronic properties such as the band structure and charge transfer. A volcano-shaped relationship is found between the absolute value of adsorption energy and energy barrier at the heteroatom Fe-TM/GP. Fe-Ni/GP has the lowest energy barrier. Heteroatom Fe-TM/GP has a lower energy barrier than Fe-Fe/GP.

Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks

Although the tailored metal active sites and porous architectures of MOFs hold great promise for engineering challenges ranging from gas separations to catalysis, a lack of understanding of how to improve their stability limits their use in practice. To overcome this limitation, we extract thousands of published reports of the key aspects of MOF stability necessary for their practical application: the ability to withstand high temperatures without degrading and the capacity to be activated by removal of solvent molecules. From nearly 4000 manuscripts, we use natural language processing and image analysis to obtain over 2000 solvent-removal stability measures and 3000 thermal degradation temperatures. We analyze the relationships between stability properties and the chemical and geometric structures in this set to identify limits of prior heuristics derived from smaller sets of MOFs. By training predictive machine learning (ML, i.e., Gaussian process and artificial neural network) models to encode the structure-property relationships with graph- and pore-structure-based representations, we are able to make predictions of stability orders of magnitude faster than conventional physics-based modeling or experiment. Interpretation of important features in ML models provides insights that we use to identify strategies to engineer increased stability into typically unstable 3d-transition-metal-containing MOFs that are frequently targeted for catalytic applications. We expect our approach to accelerate the time to discovery of stable, practical MOF materials for a wide range of applications.

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.

Beyond Radical Rebound: Methane Oxidation to Methanol Catalyzed by Iron Species in Metal-Organic Framework Nodes

Recent work has exploited the ability of metal-organic frameworks (MOFs) to isolate Fe sites that mimic the structures of sites in enzymes that catalyze selective oxidations at low temperatures, opening new pathways for the valorization of underutilized feedstocks such as methane. Questions remain as to whether the radical-rebound mechanism commonly invoked in enzymatic and homogeneous systems also applies in these rigid-framework materials, in which resisting the overoxidation of desired products is a major challenge. We demonstrate that MOFs bearing Fe(II) sites within Fe₃-μ₃-oxo nodes active for conversion of CH₄ + N₂O mixtures (368-408 K) require steps beyond the radical-rebound mechanism to protect the desired CH₃OH product. Infrared spectra and density functional theory show that CH₃OH_(g) is stabilized as Fe(III)-OCH₃ groups on the MOF via hydrogen atom transfer with Fe(III)-OH groups, eliminating water. Consequently, upon addition of a protonic zeolite in inter- and intrapellet mixtures with the MOF, we observed increases in CH₃OH selectivity with increasing ratio and proximity of zeolitic H⁺ to MOF-based Fe(II) sites, as methanol is protected within the zeolite. We infer from the data that CH₃OH_(g) is formed via the radical-rebound mechanism on Fe(II) sites but that subsequent transport and dehydration steps are required to protect CH₃OH_(g) from overoxidation. The results demonstrate that the radical-rebound mechanism commonly invoked in this chemistry is insufficient to explain the reactivity of these systems, that the selectivity-controlling steps involve both chemical and physical rate phenomena, as well as offering a strategy to mitigate overoxidation in these and similar systems.

Thermal Treatment Effect on CO and NO Adsorption on Fe(II) and Fe(III) Species in Fe3O-Based MIL-Type Metal-Organic Frameworks: A Density Functional Theory Study

The properties of metal–organic frameworks (MOFs) based on triiron oxo-centered (Fe₃O) metal nodes are often related to the efficiency of the removal of the solvent molecules and the counteranion chemisorbed on the Fe₃O unit by postsynthetic thermal treatment. Temperature, time, and the reaction environment play a significant role in modifying key features of the materials, that is, the number of open metal sites and the reduction of Fe(III) centers to Fe(II). IR spectroscopy allows the inspection of these postsynthetic modifications by using carbon monoxide (CO) and nitric oxide (NO) as probe molecules. However, the reference data sets are based on spectra recorded for iron zeolites and oxides, whose structures are different from the Fe₃O one. We used density functional theory to study how the adsorption enthalpy and the vibrational bands of CO and NO are modified upon dehydration and reduction of Fe₃O metal nodes. We obtained a set of theoretical spectra that can model the modification observed in previously reported experimental spectra. Several CO and NO bands were previously assigned to heterogeneous Fe(II) and Fe(III) sites, suggesting a large defectivity of the materials. On the basis of the calculations, we propose an alternative assignment of these bands by considering only crystallographic iron sites. These findings affect the common description of Fe₃O-based MOFs as highly defective materials. We expect these results to be of interest to the large community of scientists working on Fe(II)- and Fe(III)-based MOFs and related materials.

Thermal treatment of triiron oxo-centered (Fe₃O)-based metal−organic frameworks is a common postsynthetic method to determine the material performances in many applications: we used density functional theory methods to study how the efficacy of the treatment modifies the energetics and the vibrational bands of nitric oxide (NO) and carbon monoxide. The obtained data set is meant to be part of the characterization toolboxes aimed at the assessment of thermal treatment protocols.

Towards a unified understanding of the copper sites in particulate methane monooxygenase: an X-ray absorption spectroscopic investigation

The enzymatic conversion of the greenhouse gas, methane, to a liquid fuel, methanol, is performed by methane monooxygenases (MMOs) under mild conditions. The copper stoichiometry of particulate MMO (pMMO) has been long debated, with a dicopper site previously proposed on the basis of a 2.51 Å Cu–Cu feature in extended X-ray absorption fine structure (EXAFS) data. However, recent crystallographic data and advanced electron paramagnetic resonance (EPR) characterization support the presence of only mononuclear copper sites. To reconcile these data, we have collected high-energy resolution fluorescence detected (HERFD) and partial fluorescence yield (PFY) EXAFS spectra of Methylococcus (M.) capsulatus (Bath) pMMO. Both methods reveal only monocopper sites. These data were compared to previously published pMMO PFY-EXAFS data from M. capsulatus (Bath) and Methylomicrobium alcaliphilum 20Z, supporting dicopper and monocopper sites, respectively. The FT-EXAFS feature previously attributed to a dicopper site can be reproduced by the inclusion of a metallic copper background signal. The exact position of this feature is dependent on the nature of the sample and the percentage of background contamination, indicating that visual inspection is not sufficient for identifying background metallic contributions. Additionally, an undamaged X-ray absorption spectrum was obtained, consistent with the copper oxidation-state speciation determined by EPR quantification. X-ray photodamage studies suggest that the previously observed Cu(i) XAS features are in part attributable to photodamage. This study illustrates the complex array of factors involved in EXAFS measurement and modeling of pMMO and more generally, dilute metalloproteins with multiple metal centers.

Extended X-ray absorption fine structure spectroscopic analysis of particulate methane monooxygenase reveals only monocopper sites and investigates the possible origins of the previous observed dicopper signals.

Shifting the scaling relations of single-atom catalysts for facile methane activation by tuning the coordination number

We investigate oxidative methane activation on a wide range of single transition metal atom catalysts embedded on N-doped graphene derivatives using density functional theory calculations. An inverse scaling relationship between *O formation and its hydrogen affinity is observed, consistent with a previous report. However, we find that the latter scaling line can be shifted towards a more reactive region by tuning the coordination number (CN) of the active metal sites. Specifically, we find that lowering the CN plays an important role in increasing the reactivity for methane activation via a radical-like transition state by moving the scaling lines. Thus, in the new design strategy suggested here, different from the conventional efforts focusing mainly on breaking the scaling relations, one maintains the scaling relations but moves them towards more reactive regions by controlling the coordination number of the active sites. With this design principle, we suggest several single atom catalysts with lower C-H activation barriers than some of the most active methane activation catalysts in the literature such as Cu-based zeolites.

Defects as catalytic sites for the oxygen evolution reaction in Earth-abundant MOF-74 revealed by DFT

The oxygen evolution reaction (OER) is the bottleneck of hydrogen production via water splitting and understanding electrocatalysts at atomic level becomes paramount to enhance the efficiency of this process.

Unveiling the catalytic potential of the Fe(iv)oxo species for the oxidation of hydrocarbons in the solid state

We have investigated the three steps in the conversion of methane into methanol by Fe(iv)Ooxo species supported in MOF-74. We use ab initio MD and static approximations to predict the reaction barriers using enthalpy ΔH and free energy ΔG.

Topology-Dependent Alkane Diffusion in Zirconium Metal-Organic Frameworks

Metal-organic frameworks (MOFs) can be designed for chemical applications by modulating the size and shape of intracrystalline pores through selection of their nodes and linkers. Zirconium nodes with variable connectivity to organic linkers allow for a broad range of topological nets that have diverse pore structures even for a consistent set of linkers. Identifying an optimal pore structure for a given application, however, is complicated by the large material space of possible MOFs. In this work, molecular dynamics simulations were used to determine how a MOF's topology affects the diffusion of propane and isobutane over the full range of loadings and to understand how MOFs can be tuned to reduce transport limitations for applications in separations and catalysis. High-throughput simulation techniques were employed to efficiently calculate loading-dependent diffusivities in 38 MOFs. The results show that topologies with higher node connectivity have reduced alkane diffusivities compared to topologies with lower node connectivity. Molecular siting techniques were used to elucidate how the pore structures in different topologies affect adsorbate diffusivities.