Optimizing Open Iron Sites in Metal-Organic Frameworks for Ethane Oxidation: A First-Principles Study

Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes

The breadth of transition metal chemical space covered by databases such as the Cambridge Structural Database and the derived computational database tmQM is not conducive to application-specific modeling and the development of structure-property relationships. Here, we employ both supervised and unsupervised natural language processing (NLP) techniques to link experimentally synthesized compounds in the tmQM database to their respective applications. Leveraging NLP models, we curate four distinct datasets: tmCAT for catalysis, tmPHOTO for photophysical activity, tmBIO for biological relevance, and tmSCO for magnetism. Analyzing the chemical substructures within each dataset reveals common chemical motifs in each of the designated applications. We then use these common chemical structures to augment our initial datasets for each application, yielding a total of 21 631 compounds in tmCAT, 4599 in tmPHOTO, 2782 in tmBIO, and 983 in tmSCO. These datasets are expected to accelerate the more targeted computational screening and development of refined structure-property relationships with machine learning.

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.

Transition metal oxide complexes as molecular catalysts for selective methane to methanol transformation: any prospects or time to retire?

Transition metal oxides have been extensively used in the literature for the conversion of methane to methanol. Despite the progress made over the past decades, no method with satisfactory performance or economic viability has been detected. The main bottleneck is that the produced methanol oxidizes further due to its weaker C-H bond than that of methane. Every improvement in the efficiency of a catalyst to activate methane leads to reduction of the selectivity towards methanol. Is it therefore prudent to keep studying (both theoretically and experimentally) metal oxides as catalysts for the quantitative conversion of methane to methanol? This perspective focuses on molecular metal oxide complexes and suggests strategies to bypass the current bottlenecks with higher weight on the computational chemistry side. We first discuss the electronic structure of metal oxides, followed by assessing the role of the ligands in the reactivity of the catalysts. For better selectivity, we propose that metal oxide anionic complexes should be explored further, while hydrophylic cavities in the vicinity of the metal oxide can perturb the transition-state structure for methanol increasing appreciably the activation barrier for methanol. We also emphasize that computational studies should target the activation reaction of methanol (and not only methane), the study of complete catalytic cycles (including the recombination and oxidation steps), and the use of molecular oxygen as an oxidant. The titled chemical conversion is an excellent challenge for theory and we believe that computational studies should lead the field in the future. It is finally shown that bottom-up approaches offer a systematic way for exploration of the chemical space and should still be applied in parallel with the recently popular machine learning techniques. To answer the question of the title, we believe that metal oxides should still be considered provided that we change our focus and perform more systematic investigations on the activation of methanol.

Electrostatic potential-derived charge: a universal OER performance descriptor for MOFs

Metal-organic frameworks (MOFs) provide opportunities for the design of high-efficiency catalysts attributed to their high compositional and structural tunability. Meanwhile, the huge number of MOFs poses a great challenge to experimental-intensive development of high-performance functional applications. By taking the computationally feasible and structurally representative trigonal prismatic secondary building units (SBUs) of MOFs as the entry point, we introduce a descriptor-based approach for designing high-performance MOFs for the oxygen evolution reaction (OER). The electrostatic potential-derived charge (ESPC) is identified as a robust and universal OER performance descriptor of MOFs, showing a distinct linear relationship with the onset potentials of OER elemental steps. Importantly, we establish an ESPC-based physical pattern of active site-intermediate binding strength, which interprets the rationality of ESPC as an OER performance descriptor. We further reveal that the SBUs with Ni/Cu as active site atoms while Mn/Fe/Co/Ni as spectator atoms have excellent OER activity through the variation pattern of ESPC along with metal composition. The universal correlation between ESPC and OER activity provides a rational rule for designing high-performance MOF-based OER electrocatalysts and can be easily extended to design functional MOFs for a rich variety of catalytic applications.

Computational Analysis of Structure - Activity Relationships in Highly Active Homogeneous Ruthenium-based Water Oxidation Catalysts

Linear free energy scaling relationships (LFESRs) and regression analysis may predict the catalytic performance of heterogeneous and recently, homogenous water oxidation catalysts (WOCs). This study analyses twelve homogeneous Ru-based catalysts - some, the most active catalysts studied: the Ru(tpy-R)(QC) and Ru(tpy-R)(4-pic)2 catalysts, where tpy is 2,2:6,2-terpyridine, QC is 8-quinolinecarboxylate and 4-pic is 4-picoline. Typical relationships studied among heterogenous and solid-state catalysts cannot be broadly applied to homogeneous catalysts. This subset of structurally similar catalysts with impressive catalytic activity deserves closer computational and statistical analysis of energetics correlating with measured catalytic activity. We report general methods of LFESR analysis yield insufficiently robust relationships between descriptor variables. However, volcano plot-based analysis grounded in Sabatier's principle reveals ranges of ideal relative energies of the RuIV=O and RuIV-OH intermediates and optimal changes in free energies of water nucleophilic attack on RuV=O. A narrow range of RuIV-OH to RuV=O redox potentials corresponding with the highest catalytic activities suggests facile access to the catalytically competent high-valent RuV=O state, often inaccessible from RuIV=O. Our work introduces experimental oxygen evolution rates into approaches of LFESR and Sabatier principle-based analysis, identifying a narrow yet fertile energetic landscape to bountiful oxygen-evolution activity, leading future rational design.

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.

Machine Learning for the Discovery, Design, and Engineering of Materials

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based modes, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward (a) the discovery of new materials through large-scale enumerative screening, (b) the design of materials through identification of rules and principles that govern materials properties, and (c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Computational Scaling Relationships Predict Experimental Activity and Rate-Limiting Behavior in Homogeneous Water Oxidation

While computational screening with first-principles density functional theory (DFT) is essential for evaluating candidate catalysts, limitations in accuracy typically prevent the prediction of experimentally relevant activities. Exemplary of these challenges are homogeneous water oxidation catalysts (WOCs) where differences in experimental conditions or small changes in ligand structure can alter rate constants by over an order of magnitude. Here, we compute mechanistically relevant electronic and energetic properties for 19 mononuclear Ru transition-metal complexes (TMCs) from three experimental water oxidation catalysis studies. We discover that 15 of these TMCs have experimental activities that correlate with a single property, the ionization potential of the Ru(II)-O₂ catalytic intermediate. This scaling parameter allows the quantitative understanding of activity trends and provides insight into the rate-limiting behavior. We use this approach to rationalize differences in activity with different experimental conditions, and we qualitatively analyze the source of distinct behavior for different electronic states in the other four catalysts. Comparison to closely related single-atom catalysts and modified WOCs enables rationalization of the source of rate enhancement in these WOCs.

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.

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.

Metal–Organic Frameworks as Versatile Platforms for Organometallic Chemistry

Metal–organic frameworks (MOFs) are emerging porous materials with highly tunable structures developed in the 1990s, while organometallic chemistry is of fundamental importance for catalytic transformation in the academic and industrial world for many decades. Through the years, organometallic chemistry has been incorporated into functional MOF construction for diverse applications. Here, we will focus on how organometallic chemistry is applied in MOF design and modifications from linker-centric and metal-cluster-centric perspectives, respectively. Through structural design, MOFs can function as a tailorable platform for traditional organometallic transformations, including reaction of alkenes, cross-coupling reactions, and C–H activations. Besides, an overview will be made on other application categories of organometallic MOFs, such as gas adsorption, magnetism, quantum computing, and therapeutics.

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

The variability of chemical bonding in open-shell transition-metal complexes not only motivates their study as functional materials and catalysts but also challenges conventional computational modeling tools. Here, tailoring ligand chemistry can alter preferred spin or oxidation states as well as electronic structure properties and reactivity, creating vast regions of chemical space to explore when designing new materials atom by atom. Although first-principles density functional theory (DFT) remains the workhorse of computational chemistry in mechanism deduction and property prediction, it is of limited use here. DFT is both far too computationally costly for widespread exploration of transition-metal chemical space and also prone to inaccuracies that limit its predictive performance for localized d electrons in transition-metal complexes. These challenges starkly contrast with the well-trodden regions of small-organic-molecule chemical space, where the analytical forms of molecular mechanics force fields and semiempirical theories have for decades accelerated the discovery of new molecules, accurate DFT functional performance has been demonstrated, and gold-standard methods from correlated wavefunction theory can predict experimental results to chemical accuracy.The combined promise of transition-metal chemical space exploration and lack of established tools has mandated a distinct approach. In this Account, we outline the path we charted in exploration of transition-metal chemical space starting from the first machine learning (ML) models (i.e., artificial neural network and kernel ridge regression) and representations for the prediction of open-shell transition-metal complex properties. The distinct importance of the immediate coordination environment of the metal center as well as the lack of low-level methods to accurately predict structural properties in this coordination environment first motivated and then benefited from these ML models and representations. Once developed, the recipe for prediction of geometric, spin state, and redox potential properties was straightforwardly extended to a diverse range of other properties, including in catalysis, computational "feasibility", and the gas separation properties of periodic metal-organic frameworks. Interpretation of selected features most important for model prediction revealed new ways to encapsulate design rules and confirmed that models were robustly mapping essential structure-property relationships. Encountering the special challenge of ensuring that good model performance could generalize to new discovery targets motivated investigation of how to best carry out model uncertainty quantification. Distance-based approaches, whether in model latent space or in carefully engineered feature space, provided intuitive measures of the domain of applicability. With all of these pieces together, ML can be harnessed as an engine to tackle the large-scale exploration of transition-metal chemical space needed to satisfy multiple objectives using efficient global optimization methods. In practical terms, bringing these artificial intelligence tools to bear on the problems of transition-metal chemical space exploration has resulted in ML-model assessments of large, multimillion compound spaces in minutes and validated new design leads in weeks instead of decades.

Active metal single-sites based on metal–organic frameworks: construction and chemical prospects

Metal single-point is a novel and potential design strategy that has been applied for the development of metal organic frameworks.

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.

Exploring the Tunability of Trimetallic MOF Nodes for Partial Oxidation of Methane to Methanol

Density functional theory is used to study the tunability of trigonal prismatic SBUs found in metal-organic frameworks (MOFs) such as MIL-100, MIL-101, and PCN-250/MIL-127 of chemical composition M³⁺₂M²⁺(μ₃-O)(RCOO)₆ for the partial oxidation of methane to methanol. We performed a combinatorial screening by varying the composition of the trimetallic node (M₁³⁺)₂(M₂²⁺) (where M₁ and M₂ = V, Cr, Mn, Fe, Co, and Ni) and calculated the reaction pathway on both M₁ and M₂ sites. The systematic replacement of metals in the trimetallic cluster allowed us to study the influence of spectator atoms on the catalytic activity of a specific metal site in the cluster toward the N₂O activation and C-H bond activation steps of the reaction. In the screening, we identified the top-performing node compositions with predicted barriers lower than those already reported for experimentally tested MOFs with trigonal prismatic SBUs. This work demonstrates the opportunity to tune the catalytic activity of MOFs for redox reactions by changing their metal node composition.

DFT Study on the Catalytic Activity of ALD-Grown Diiron Oxide Nanoclusters for Partial Oxidation of Methane to Methanol

Using density functional theory (DFT), we studied the catalytic activity of iron oxide nanoclusters that mimic the structure of the active site in the soluble form of methane monooxygenase (sMMO) for the partial oxidation of methane to methanol. Using N₂O as the oxidant, we consider a radical-rebound mechanism and a concerted mechanism for the oxidation of methane on either a bridging oxygen (O_b) or a terminal oxygen (O_t) active site. We find that the radical-rebound pathway is preferred over the concerted pathway by 40-50 kJ/mol, but the desorption of methanol and the regeneration of the oxygen site are found to be the highest barriers for the direct conversion of methane to methanol with these catalysts. As demonstrated by a population analysis, the O_x (x = b or t) site behaves as an oxygen radical during the H abstraction, and the [Fe⁺-O_x^-] site behaves as a Lewis acid-base pair during the concerted C-H cleavage. Molecular orbital decomposition analysis further demonstrates electron transfer during the oxidation and reduction steps of the reaction. High-level multireference calculations were also carried out to further assess the DFT results. Understanding how these systems behave during the proposed reaction pathways provides new insights into how they can be tuned for methane partial oxidation.

Electronic effects due to organic linker-metal surface interactions: implications on screening of MOF-encapsulated catalysts

Using approximated NP/MOF interface models, DFT was used to investigate MOF-originated electronic effects on encapsulated NPs in NP@MOF hybrid catalysts.

Selective Methane Oxidation to Methanol on Cu-Oxo Dimers Stabilized by Zirconia Nodes of an NU-1000 Metal-Organic Framework

Mononuclear and dinuclear copper species were synthesized at the nodes of an NU-1000 metal-organic framework (MOF) via cation exchange and subsequent oxidation at 200 °C in oxygen. Copper-exchanged MOFs are active for selectively converting methane to methanol at 150-200 °C. At 150 °C and 1 bar methane, approximately a third of the copper centers are involved in converting methane to methanol. Methanol productivity increased by 3-4-fold and selectivity increased from 70% to 90% by increasing the methane pressure from 1 to 40 bar. Density functional theory showed that reaction pathways on various copper sites are able to convert methane to methanol, the copper oxyl sites with much lower free energies of activation. Combining studies of the stoichiometric activity with characterization by in situ X-ray absorption spectroscopy and density functional theory, we conclude that dehydrated dinuclear copper oxyl sites formed after activation at 200 °C are responsible for the activity.

Computational Approach to Molecular Catalysis by 3d Transition Metals: Challenges and Opportunities

Computational chemistry provides a versatile toolbox for studying mechanistic details of catalytic reactions and holds promise to deliver practical strategies to enable the rational in silico catalyst design. The versatile reactivity and nontrivial electronic structure effects, common for systems based on 3d transition metals, introduce additional complexity that may represent a particular challenge to the standard computational strategies. In this review, we discuss the challenges and capabilities of modern electronic structure methods for studying the reaction mechanisms promoted by 3d transition metal molecular catalysts. Particular focus will be placed on the ways of addressing the multiconfigurational problem in electronic structure calculations and the role of expert bias in the practical utilization of the available methods. The development of density functionals designed to address transition metals is also discussed. Special emphasis is placed on the methods that account for solvation effects and the multicomponent nature of practical catalytic systems. This is followed by an overview of recent computational studies addressing the mechanistic complexity of catalytic processes by molecular catalysts based on 3d metals. Cases that involve noninnocent ligands, multicomponent reaction systems, metal-ligand and metal-metal cooperativity, as well as modeling complex catalytic systems such as metal-organic frameworks are presented. Conventionally, computational studies on catalytic mechanisms are heavily dependent on the chemical intuition and expert input of the researcher. Recent developments in advanced automated methods for reaction path analysis hold promise for eliminating such human-bias from computational catalysis studies. A brief overview of these approaches is presented in the final section of the review. The paper is closed with general concluding remarks.

A generalized van der Waals model for light gas adsorption prediction in IRMOFs

A generalized van der Waals model for predicting the adsorption isotherms in IRMOFs by defining different attractive regions.

Metal-Organic Framework (MOF)-Based Materials as Heterogeneous Catalysts for C-H Bond Activation

Converting light hydrocarbons such as methane, ethane, propane, and cyclohexane into value-added chemicals and fuel products by means of direct C-H functionalization is an attractive method in the petrochemical industry. As they emerge as a relatively new class of porous solid materials, metal-organic frameworks (MOFs) are appealing as single-site heterogeneous catalysts or catalytic supports for C-H bond activation. In contrast to the traditional microporous and mesoporous materials, MOFs feature high porosity, functional tunability, and molecular-level characterization for the study of structure-property relationships. These virtues make MOFs ideal platforms to develop catalysts for C-H activation with high catalytic activity, selectivity, and recyclability under relatively mild reaction conditions. This review highlights the research aimed at the implementation of MOFs as single-site heterogeneous catalysts for C-H bond activation. It provides insight into the rational design and synthesis of three types of stable MOF catalysts for C-H bond activation, that is, i) metal nodes as catalytic sites, ii) the incorporation of catalytic sites into organic struts, and iii) the incorporation of catalytically active guest species into pores of MOFs. Here, the rational design and synthesis of MOF catalysts that lead to the distinct catalytic property for C-H bond activation are discussed along with the post-synthesis of MOFs, intriguing functions with MOF catalysts, and microenvironments that lead to the distinct catalytic properties of MOF catalysts.

Ligand field effects on the ground and excited states of reactive FeO2+ species

Multiconfigurational quantum chemical calculations on bare and representative ligated iron oxide dicationic species suggest that weak ligand fields promote more reactive channels, whereas strong ligand fields stabilize the less reactive iron-oxo structure.