Modeling Operando Electrochemical CO2 Reduction

Since the seminal works on the application of density functional theory and the computational hydrogen electrode to electrochemical CO₂ reduction (eCO₂R) and hydrogen evolution (HER), the modeling of both reactions has quickly evolved for the last two decades. Formulation of thermodynamic and kinetic linear scaling relationships for key intermediates on crystalline materials have led to the definition of activity volcano plots, overpotential diagrams, and full exploitation of these theoretical outcomes at laboratory scale. However, recent studies hint at the role of morphological changes and short-lived intermediates in ruling the catalytic performance under operating conditions, further raising the bar for the modeling of electrocatalytic systems. Here, we highlight some novel methodological approaches employed to address eCO₂R and HER reactions. Moving from the atomic scale to the bulk electrolyte, we first show how ab initio and machine learning methodologies can partially reproduce surface reconstruction under operation, thus identifying active sites and reaction mechanisms if coupled with microkinetic modeling. Later, we introduce the potential of density functional theory and machine learning to interpret data from Operando spectroelectrochemical techniques, such as Raman spectroscopy and extended X-ray absorption fine structure characterization. Next, we review the role of electrolyte and mass transport effects. Finally, we suggest further challenges for computational modeling in the near future as well as our perspective on the directions to follow.

Eliminating Delocalization Error to Improve Heterogeneous Catalysis Predictions with Molecular DFT + U

Approximate semilocal density functional theory (DFT) is known to underestimate surface formation energies yet paradoxically overbind adsorbates on catalytic transition-metal oxide surfaces due to delocalization error. The low-cost DFT + U approach only improves surface formation energies for early transition-metal oxides or adsorption energies for late transition-metal oxides. In this work, we demonstrate that this inefficacy arises due to the conventional usage of metal-centered atomic orbitals as projectors within DFT + U. We analyze electron density rearrangement during surface formation and O atom adsorption on rutile transition-metal oxides to highlight that a standard DFT + U correction fails to tune properties when the corresponding density rearrangement is highly delocalized across both metal and oxygen sites. To improve both surface properties simultaneously while retaining the simplicity of a single-site DFT + U correction, we systematically construct multi-atom-centered molecular-orbital-like projectors for DFT + U. We demonstrate this molecular DFT + U approach for tuning adsorption energies and surface formation energies of minimal two-dimensional models of representative early (i.e., TiO₂) and late (i.e., PtO₂) transition-metal oxides. Molecular DFT + U simultaneously corrects adsorption energies and surface formation energies of multilayer models of rutile TiO₂(110) and PtO₂(110) to resolve the paradoxical description of surface stability and surface reactivity of semilocal DFT.

One Decade of Computational Studies on Single-Atom Alloys: Is In Silico Design within Reach?

ConspectusSingle-Atom alloys (SAAs) are an emerging class of materials consisting of a coinage metal (Cu, Ag, and Au) doped, at the single-atom limit, with another metal. As catalysts, coinage metals are rarely very active on their own, but when they are, they exhibit high selectivity. On the other hand, transition metals are usually very active but not as selective. Incorporating transition metals (guest elements) into coinage metals (host material) is therefore appealing for combining the activity and selectivity of each constituent in a balanced way. Additionally, first-principles calculations have shown that single atoms embedded in the surface of a coinage metal can exhibit emergent properties. Here, we describe how computational studies based on density functional theory (DFT) and kinetic Monte Carlo (KMC) simulations, often undertaken in close collaboration with experimental research groups, have shaped, over the past decade, the way we understand SAA catalysis.This Account reviews our contributions in elucidating the stability of SAAs, their electronic structure, and the way adsorbates interact and react on SAA catalytic surfaces. By studying in detail the processes that affect the stability of the SAA phase, we have shown that out of several bimetallic combinations of coinage metals with prominent Pt-group metals only PtCu and PdCu are stable surface alloys under vacuum. However, more surface alloy structures are possible in the presence of adsorbates because the latter can stabilize, via strong binding, dopants in the surface of the material. More interestingly, a large number of these surface alloys are resistant to the aggregation of dopant atoms into clusters, thereby favoring the SAA structure. These major results from DFT calculations serve as a guide for experimentalists to explore new SAA catalysts. Further analysis has shown that SAAs have a unique electronic structure with a very sharp d-band feature close to the Fermi level, analogous to the electronic structure of molecular entities. This is one of the reasons that SAAs are particularly sought after: although they are metallic nanoparticles, they have properties akin to those of homogeneous catalysts. In this context, we have contributed extensive screening studies, focusing on molecular fragments of catalytic relevance on a range of SAAs, which have driven the identification of new catalysts. We have also explored the rich chemistry of two-adsorbate systems via kinetic modeling, demonstrating how a spectator species with greater affinity for the dopant can modulate the reactivity of the catalyst via the so-called (punctured) molecular cork effect.Since the first experimental characterization of SAAs about a decade ago, theoretical models have been able to support and explain various experimental observations. These models have served as benchmarks for assessing the predictive capability of the underlying theoretical methods. In turn, the predictions that have been delivered have guided and continue to guide the experimental research efforts in the field. These advancements show that the in silico design of new SAA catalysts is now within reach.

Kinetic Monte Carlo simulations of the dry reforming of methane catalyzed by the Ru (0001) surface based on density functional theory calculations

This study shows the main pathways for the DRM reaction and the competitive RWGS reaction upon changing reaction conditions, displaying the importance of including lateral–lateral interactions to describe the reaction in agreement with the experiment.

Unveiling gas-phase oxidative coupling of methane via data analysis

Unveiling the details of the mechanisms of a chemical reaction is a difficult task as reaction mechanisms are strongly coupled with reaction conditions. Here, catalysts informatics combined with high-throughput experimental data is implemented to understand the oxidative coupling of methane (OCM) reaction. In particular, pairwise correlation and data visualization are performed to reveal the relation between reaction conditions and selectivity/conversion. In addition, machine learning is used to fill the gap between experimental data points; thus, a more detailed understanding of the OCM reaction against reaction conditions can be achieved. Therefore, catalysts informatics is proposed for understanding the details of the reaction mechanism, thereby aiding reaction design.

Reaction mechanisms at the homogeneous–heterogeneous frontier: insights from first-principles studies on ligand-decorated metal nanoparticles

The frontiers between homogeneous and heterogeneous catalysis are progressively disappearing.

An Active Alkali-Exchanged Faujasite Catalyst for p-Xylene Production via the One-Pot Diels-Alder Cycloaddition/Dehydration Reaction of 2,5-Dimethylfuran with Ethylene

The one-pot Diels-Alder cycloaddition (DAC)/dehydration (D) tandem reaction between 2,5-dimethylfuran and ethylene is a potent pathway toward biomass-derived p-xylene. In this work, we present a cheap and active low-silica potassium-exchanged faujasite (KY, Si/Al = 2.6) catalyst. Catalyst optimization was guided by a computational study of the DAC/D reaction mechanism over different alkali-exchanged faujasites using periodic density functional theory calculations complemented by microkinetic modeling. Two types of faujasite models were compared, i.e., a high-silica alkali-exchanged faujasite model representing isolated active cation sites and a low-silica alkali-exchanged faujasite in which the reaction involves several cations in the proximity. The mechanistic study points to a significant synergetic cooperative effect of the ensemble of cations in the faujasite supercage on the DAC/D reaction. Alignment of the reactants by their interactions with the cationic sites and stabilization of reaction intermediates contribute to the high catalytic performance. Experiments confirmed the prediction that KY is the most active catalyst among low-silica alkali-exchanged faujasites. This work is an example of how the catalytic reactivity of zeolites depends on multiple interactions between the zeolite and reagents.

Computational Design of Functionalized Metal-Organic Framework Nodes for Catalysis

Recent progress in the synthesis and characterization of metal-organic frameworks (MOFs) has opened the door to an increasing number of possible catalytic applications. The great versatility of MOFs creates a large chemical space, whose thorough experimental examination becomes practically impossible. Therefore, computational modeling is a key tool to support, rationalize, and guide experimental efforts. In this outlook we survey the main methodologies employed to model MOFs for catalysis, and we review selected recent studies on the functionalization of their nodes. We pay special attention to catalytic applications involving natural gas conversion.

Lattice mismatch as the descriptor of segregation, stability and reactivity of supported thin catalyst films

Surface properties of supported bimetallic films can be predicted from the lattice mismatch between the overlayer and the support already there for trilayers.

Future Challenges in Heterogeneous Catalysis: Understanding Catalysts under Dynamic Reaction Conditions

In the future, (electro-)chemical catalysts will have to be more tolerant towards a varying supply of energy and raw materials. This is mainly due to the fluctuating nature of renewable energies. For example, power-to-chemical processes require a shift from steady-state operation towards operation under dynamic reaction conditions. This brings along a number of demands for the design of both catalysts and reactors, because it is well-known that the structure of catalysts is very dynamic. However, in-depth studies of catalysts and catalytic reactors under such transient conditions have only started recently. This requires studies and advances in the fields of 1) operando spectroscopy including time-resolved methods, 2) theory with predictive quality, 3) kinetic modelling, 4) design of catalysts by appropriate preparation concepts, and 5) novel/modular reactor designs. An intensive exchange between these scientific disciplines will enable a substantial gain of fundamental knowledge which is urgently required. This concept article highlights recent developments, challenges, and future directions for understanding catalysts under dynamic reaction conditions.

Theoretical Heterogeneous Catalysis: Scaling Relationships and Computational Catalyst Design

Scaling relationships are theoretical constructs that relate the binding energies of a wide variety of catalytic intermediates across a range of catalyst surfaces. Such relationships are ultimately derived from bond order conservation principles that were first introduced several decades ago. Through the growing power of computational surface science and catalysis, these concepts and their applications have recently begun to have a major impact in studies of catalytic reactivity and heterogeneous catalyst design. In this review, the detailed theory behind scaling relationships is discussed, and the existence of these relationships for catalytic materials ranging from pure metal to oxide surfaces, for numerous classes of molecules, and for a variety of catalytic surface structures is described. The use of the relationships to understand and elucidate reactivity trends across wide classes of catalytic surfaces and, in some cases, to predict optimal catalysts for certain chemical reactions, is explored. Finally, the observation that, in spite of the tremendous power of scaling relationships, their very existence places limits on the maximum rates that may be obtained for the catalyst classes in question is discussed, and promising strategies are explored to overcome these limitations to usher in a new era of theory-driven catalyst design.

Direct arylation and heterogeneous catalysis; ever the twain shall meet

The formation of aryl-aryl bonds and heteroaryl analogues is one of the most important C-C bond forming processes in organic chemistry. Recently, a methodology termed Direct Arylation (DA) has emerged as an attractive alternative to traditional cross-coupling reactions (Suzuki-Miyaura, Stille, Negishi, etc.). A parallel focus of the pharmaceutical and other chemical industries has been on the use heterogeneous catalysis as a favourable substitute for its homogeneous counterpart in cross-coupling reactions. Only very recently has heterogeneous catalysis been proposed and applied, to DA reactions. In this perspective, we consider the terms 'heterogeneous' and 'homogeneous' and the problems associated with their delineation in transition-metal catalysed reactions. We highlight the reports at the interface of DA and heterogeneous catalysis and we comment briefly on the methods used which attempt to classify reaction types as homo- or heterogeneous. In future work we recommend an emphasis be placed on kinetic methods which provide an excellent platform for analysis. In addition two analytical techniques are described which if developed to run in situ with DA reactions would illuminate our understanding of the catalysis. Overall, we provide an entry point, and bring together the mature, yet poorly-understood, subject of heterogeneous catalysis with the rapidly expanding area of DA, with a view towards the acceleration of catalyst design and the understanding of catalyst behaviour.

Advances in theory and their application within the field of zeolite chemistry

Zeolites are versatile and fascinating materials which are vital for a wide range of industries, due to their unique structural and chemical properties, which are the basis of applications in gas separation, ion exchange and catalysis. Given their economic impact, there is a powerful incentive for smart design of new materials with enhanced functionalities to obtain the best material for a given application. Over the last decades, theoretical modeling has matured to a level that model guided design has become within reach. Major hurdles have been overcome to reach this point and almost all contemporary methods in computational materials chemistry are actively used in the field of modeling zeolite chemistry and applications. Integration of complementary modeling approaches is necessary to obtain reliable predictions and rationalizations from theory. A close synergy between experimentalists and theoreticians has led to a deep understanding of the complexity of the system at hand, but also allowed the identification of shortcomings in current theoretical approaches. Inspired by the importance of zeolite characterization which can now be performed at the single atom and single molecule level from experiment, computational spectroscopy has grown in importance in the last decade. In this review most of the currently available modeling tools are introduced and illustrated on the most challenging problems in zeolite science. Directions for future model developments will be given.

First principle chemical kinetics in zeolites: the methanol-to-olefin process as a case study

To optimally design next generation catalysts a thorough understanding of the chemical phenomena at the molecular scale is a prerequisite. Apart from qualitative knowledge on the reaction mechanism, it is also essential to be able to predict accurate rate constants. Molecular modeling has become a ubiquitous tool within the field of heterogeneous catalysis. Herein, we review current computational procedures to determine chemical kinetics from first principles, thus by using no experimental input and by modeling the catalyst and reacting species at the molecular level. Therefore, we use the methanol-to-olefin (MTO) process as a case study to illustrate the various theoretical concepts. This process is a showcase example where rational design of the catalyst was for a long time performed on the basis of trial and error, due to insufficient knowledge of the mechanism. For theoreticians the MTO process is particularly challenging as the catalyst has an inherent supramolecular nature, for which not only the Brønsted acidic site is important but also organic species, trapped in the zeolite pores, must be essentially present during active catalyst operation. All these aspects give rise to specific challenges for theoretical modeling. It is shown that present computational techniques have matured to a level where accurate enthalpy barriers and rate constants can be predicted for reactions occurring at a single active site. The comparison with experimental data such as apparent kinetic data for well-defined elementary reactions has become feasible as current computational techniques also allow predicting adsorption enthalpies with reasonable accuracy. Real catalysts are truly heterogeneous in a space- and time-like manner. Future theory developments should focus on extending our view towards phenomena occurring at longer length and time scales and integrating information from various scales towards a unified understanding of the catalyst. Within this respect molecular dynamics methods complemented with additional techniques to simulate rare events are now gradually making their entrance within zeolite catalysis. Recent applications have already given a flavor of the benefit of such techniques to simulate chemical reactions in complex molecular environments.

Density functional theory simulations of complex catalytic materials in reactive environments: beyond the ideal surface at low coverage

Advanced DFT models of complex catalysts, such as amorphous silica–alumina and supported subnanometric platinum particles, bridge the gap between the ideal surface model and the industrial catalyst.

FORMIC ACID DEHYDROGENATION ON SURFACES — A REVIEW OF COMPUTATIONAL ASPECT

In this review, we have mainly shown the recent computational studies on formic acid adsorption and selective dissociation to produce hydrogen ( HCOOH → CO 2 + H 2) on several metal ( Pt , Pd , Ni , Cu , Rh and Au ) and metal oxide ( TiO 2, MgO , ZnO and NiO ) surfaces, and both thermal decomposition and electro-catalytic oxidation have been discussed. The decomposition mechanisms of formic acid have been studied by using different computational models and methods, not only interesting and exciting but also different and controversial results have been reported. It is noted that the model systems used in these studies are too simple and idealized, and they cannot represent the real catalysts or the catalytic systems, and more sophisticated computational methodologies and real model systems under the consideration of the working conditions are therefore needed.