Ten-electron count rule for the binding of adsorbates on single-atom alloy catalysts

Single-atom alloys have recently emerged as highly active and selective alloy catalysts. Unlike pure metals, single-atom alloys escape the well-established conceptual framework developed nearly three decades ago for predicting catalytic performance. Although this offers the opportunity to explore so far unattainable chemistries, this leaves us without a simple guide for the design of single-atom alloys able to catalyse targeted reactions. Here, based on thousands of density functional theory calculations, we reveal a 10-electron count rule for the binding of adsorbates on the dopant atoms, usually the active sites, of single-atom alloy surfaces. A simple molecular orbital approach rationalizes this rule and the nature of the adsorbate-dopant interaction. In addition, our intuitive model can accelerate the rational design of single-atom alloy catalysts. Indeed, we illustrate how the unique insights provided by the electron count rule help identify the most promising dopant for an industrially relevant hydrogenation reaction, thereby reducing the number of potential materials by more than one order of magnitude.

Transition Metal Carbides as Supports for Catalytic Metal Particles: Recent Progress and Opportunities

Transition metal carbides (TMCs) constitute excellent alternatives to traditional oxide-based supports for small metal particles, leading to strong metal-support interactions, which drastically modify the catalytic properties of the supported metal atoms. Moreover, they possess extremely high melting points and good resistance to carbon deposition and sulfur poisoning, and the catalytic activities of some TMCs per se have been shown to be similar to those of Pt-group metals for a considerable number of reactions. Therefore, the use of TMCs as supports can give rise to bifunctional catalysts with multiple active sites. However, at present, only TiC and MoxC have been tested experimentally as supports for metal particles, and it is largely unclear which combinations may best catalyze which chemical reactions. In this Perspective, we review the most significant works on the use of TMCs as supports for catalytic applications, assess the current status of the field, and identify key advances being made and challenges, with an eye to the future.

Elucidating the Reactivity of Oxygenates on Single-Atom Alloy Catalysts

Doping isolated transition metal atoms into the surface of coinage-metal hosts to form single-atom alloys (SAAs) can significantly improve the catalytic activity and selectivity of their monometallic counterparts. These atomically dispersed dopant metals on the SAA surface act as highly active sites for various bond coupling and activation reactions. In this study, we investigate the catalytic properties of SAAs with different bimetallic combinations [Ni-, Pd-, Pt-, and Rh-doped Cu(111), Ag(111), and Au(111)] for chemistries involving oxygenates relevant to biomass reforming. Density functional theory is employed to calculate and compare the formation energies of species such as methoxy (CH3O), methanol (CH3OH), and hydroxymethyl (CH2OH), thereby understanding the stability of these adsorbates on SAAs. Activation energies and reaction energies of C-O coupling, C-H activation, and O-H activation on these oxygenates are then computed. Analysis of the data in terms of thermochemical linear scaling and Bro̷nsted-Evans-Polanyi relationship shows that some SAAs have the potential to combine weak binding with low activation energies, thereby exhibiting enhanced catalytic behavior over their monometallic counterparts for key elementary steps of oxygenate conversion. This work contributes to the discovery and development of SAA catalysts toward greener technologies, having potential applications in the transition from fossil to renewable fuels and chemicals.

Speeding up the Detection of Adsorbate Lateral Interactions in Graph-Theoretical Kinetic Monte Carlo Simulations

Kinetic Monte Carlo (KMC) has become an indispensable tool in heterogeneous catalyst discovery, but realistic simulations remain computationally demanding on account of the need to capture complex and long-range lateral interactions between adsorbates. The Zacros software package (https://zacros.org) adopts a graph-theoretical cluster expansion (CE) framework that allows such interactions to be computed with a high degree of generality and fidelity. This involves solving a series of subgraph isomorphism problems in order to identify relevant interaction patterns in the lattice. In an effort to reduce the computational burden, we have adapted two well-known subgraph isomorphism algorithms, namely, VF2 and RI, for use in KMC simulations and implemented them in Zacros. To benchmark their performance, we simulate a previously established model of catalytic NO oxidation, treating the O* lateral interactions with a series of progressively larger CEs. For CEs with long-range interactions, VF2 and RI are found to provide impressive speedups relative to simpler algorithms. RI performs best, giving speedups reaching more than 150× when combined with OpenMP parallelization. We also simulate a recently developed methane cracking model, showing that RI offers significant improvements in performance at high surface coverages.

Investigating Spillover Energy as a Descriptor for Single-Atom Alloy Catalyst Design

The identification of thermodynamic descriptors of catalytic performance is essential for the rational design of heterogeneous catalysts. Here, we investigate how spillover energy, a descriptor quantifying whether intermediates are more stable at the dopant or host metal sites, can be used to design single-atom alloys (SAAs) for formic acid dehydrogenation. Using theoretical calculations, we identify NiCu as a SAA with favorable spillover energy and demonstrate that formate intermediates produced after the initial O-H activation are more stable at Ni sites where rate-determining C-H activation occurs. Surface science experiments demonstrated that NiCu(111) SAAs are more reactive than Cu(111) while they still follow the formate reaction pathway. However, reactor studies of silica-supported NiCu SAA nanoparticles showed only a modest improvement over Cu resulting from surface coverage effects. Overall, this study demonstrates the potential of engineering SAAs using spillover energy as a design parameter and highlights the importance of adsorbate-adsorbate interactions under steady-state operation.

Exact distributed kinetic Monte Carlo simulations for on-lattice chemical kinetics: lessons learnt from medium- and large-scale benchmarks

Kinetic Monte Carlo (KMC) simulations have been instrumental in multiscale catalysis studies, enabling the elucidation of the complex dynamics of heterogeneous catalysts and the prediction of macroscopic performance metrics, such as activity and selectivity. However, the accessible length- and time-scales have been a limiting factor in such simulations. For instance, handling lattices containing millions of sites with 'traditional' sequential KMC implementations is prohibitive owing to large memory requirements and long simulation times. We have recently established an approach for exact, distributed, lattice-based simulations of catalytic kinetics which couples the Time-Warp algorithm with the Graph-Theoretical KMC framework, enabling the handling of complex adsorbate lateral interactions and reaction events within large lattices. In this work, we develop a lattice-based variant of the Brusselator system, a prototype chemical oscillator pioneered by Prigogine and Lefever in the late 60s, to benchmark and demonstrate our approach. This system can form spiral wave patterns, which would be computationally intractable with sequential KMC, while our distributed KMC approach can simulate such patterns 15 and 36 times faster with 625 and 1600 processors, respectively. The medium- and large-scale benchmarks thus conducted, demonstrate the robustness of the approach, and reveal computational bottlenecks that could be targeted in further development efforts. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'.

Stability and reactivity of metal nanoclusters supported on transition metal carbides

Small particles of transition metals (TM) supported on transition metal carbides (TMC) - TM_n@TMC - provide a plethora of design opportunities for catalytic applications due to their highly exposed active centres, efficient atom utilisation and the physicochemical properties of the TMC support. To date, however, only a very small subset of TM_n@TMC catalysts have been tested experimentally and it is unclear which combinations may best catalyse which chemical reactions. Herein, we develop a high-throughput screening approach to catalyst design for supported nanoclusters based on density functional theory, and apply it to elucidate the stability and catalytic performance of all possible combinations between 7 monometallic nanoclusters (Rh, Pd, Pt, Au, Co, Ni and Cu) and 11 stable support surfaces of TMCs with 1 : 1 stoichiometry (TiC, ZrC, HfC, VC, NbC, TaC, MoC and WC) towards CH₄ and CO₂ conversion technologies. We analyse the generated database to unravel trends or simple descriptors in their resistance towards metal aggregate formation and sintering, oxidation, stability in the presence of adsorbate species, and study their adsorptive and catalytic properties, to facilitate the discovery of novel materials in the future. We identify 8 TM_n@TMC combinations as promising catalysts, all of them being new for experimental validation, thus expanding the chemical space for efficient conversion of CH₄ and CO₂.

Microscopic insights on clathrate hydrate growth from non-equilibrium molecular dynamics simulations

Clathrate hydrates form and grow at interfaces. Understanding the relevant molecular processes is crucial for developing hydrate-based technologies. Many computational studies focus on hydrate growth within the aqueous phase using the 'direct coexistence method', which is limited in its ability to investigate hydrate film growth at hydrocarbon-water interfaces. To overcome this shortcoming, a new simulation setup is presented here, which allows us to study the growth of a methane hydrate nucleus in a system where oil-water, hydrate-water, and hydrate-oil interfaces are all simultaneously present, thereby mimicking experimental setups. Using this setup, hydrate growth is studied here under the influence of two additives, a polyvinylcaprolactam oligomer and sodium dodecyl sulfate, at varying concentrations. Our results confirm that hydrate films grow along the oil-water interface, in general agreement with visual experimental observations; growth, albeit slower, also occurs at the hydrate-water interface, the interface most often interrogated via simulations. The results obtained demonstrate that the additives present within curved interfaces control the solubility of methane in the aqueous phase, which correlates with hydrate growth rate. Building on our simulation insights, we suggest that by combining data for the potential of mean force profile for methane transport across the oil-water interface and for the average free energy required to perturb a flat interface, it is possible to predict the performance of additives used to control hydrate growth. These insights could be helpful to achieve optimal methane storage in hydrates, one of many applications which are attracting significant fundamental and applied interests.

Stochastic Cellular Automata Modeling of CO2 Hydrate Growth and Morphology

Carbon dioxide (CO₂) hydrates are important in a diverse range of applications and technologies in the environmental and energy fields. The development of such technologies relies on fundamental understanding, which necessitates not only experimental but also computational studies of the growth behavior of CO₂ hydrates and the factors affecting their crystal morphology. As experimental observations show that the morphology of CO₂ hydrate particles differs depending on growth conditions, a detailed understanding of the relation between the hydrate structure and growth conditions would be helpful. To this end, this work adopts a modeling approach based on hybrid probabilistic cellular automata to investigate variations in CO₂ hydrate crystal morphology during hydrate growth from stagnant liquid water presaturated with CO₂. The model, which uses free energy density profiles as inputs, correlates the variations in growth morphology to the system subcooling ΔT, i.e., the temperature deficiency from the triple CO₂-hydrate-water equilibrium temperature under a given pressure, and properties of the growing hydrate-water interface, such as surface tension and curvature. The model predicts that when ΔT is large, parabolic needle-like or dendrite crystals emerge from planar fronts that deform and lose stability. In agreement with chemical diffusion-limited growth, the position of such planar fronts versus time follows a power law. In contrast, the tips of the emerging parabolic crystals steadily grow in proportion to time. The modeling framework is computationally fast and produces complex growth morphology phenomena under diffusion-controlled growth from simple, easy-to-implement rules, opening the way for employing it in multiscale modeling of gas hydrates.

Assessing the Impact of Adlayer Description Fidelity on Theoretical Predictions of Coking on Ni(111) at Steam Reforming Conditions

Methane steam reforming is an important industrial process for hydrogen production, employing Ni as a low-cost, highly active catalyst, which, however, suffers from coking due to methane cracking. Coking is the accumulation of a stable poison over time, occurring at high temperatures; thus, to a first approximation, it can be treated as a thermodynamic problem. In this work, we developed an Ab initio kinetic Monte Carlo (KMC) model for methane cracking on Ni(111) at steam reforming conditions. The model captures C-H activation kinetics in detail, while graphene sheet formation is described at the level of thermodynamics, to obtain insights into the "terminal (poisoned) state" of graphene/coke within reasonable computational times. We used cluster expansions (CEs) of progressively higher fidelity to systematically assess the influence of effective cluster interactions between adsorbed or covalently bonded C and CH species on the "terminal state" morphology. Moreover, we compared the predictions of KMC models incorporating these CEs into mean-field microkinetic models in a consistent manner. The models show that the "terminal state" changes significantly with the level of fidelity of the CEs. Furthermore, high-fidelity simulations predict C-CH island/rings that are largely disconnected at low temperatures but completely encapsulate the Ni(111) surface at high temperatures.

Large-scale benchmarks of the time-warp/graph-theoretical kinetic Monte Carlo approach for distributed on-lattice simulations of catalytic kinetics

Motivated by the need to perform large-scale kinetic Monte Carlo (KMC) simulations, in the context of unravelling complex phenomena such as catalyst reconstruction and pattern formation, we extend the work of Ravipati et al. [S. Ravipati, G. D. Savva, I.-A. Christidi, R. Guichard, J. Nielsen, R. Réocreux and M. Stamatakis, Comput. Phys. Commun., 2022, 270, 108148] in benchmarking the performance of a distributed-computing, on-lattice KMC approach. The latter, implemented in our software package Zacros, combines the graph-theoretical KMC framework with the Time-Warp algorithm for parallel discrete event simulations, and entails dividing the lattice into subdomains, each assigned to a processor. The cornerstone of the Time-Warp algorithm is the state queue, to which snapshots of the simulation state are saved regularly, enabling historical KMC information to be corrected when conflicts occur at subdomain boundaries. Focusing on three model systems, we highlight the key Time-Warp parameters that can be tuned to optimise performance. The frequency of state saving, controlled by the state saving interval, δ_snap, is shown to have the largest effect on performance, which favours balancing the overhead of re-simulating KMC history with that of writing state snapshots to memory. Also important is the global virtual time (GVT) computation interval, Δτ_GVT, which has little direct effect on the progress of the simulation but controls how often the state queue memory can be freed up. We also find that pre-allocating memory for the state queue data structure favours performance. These findings will guide users in maximising the efficiency of Zacros or other distributed KMC software, which is a vital step towards realising accurate, meso-scale simulations of heterogeneous catalysis.

Wetting Properties of Clathrate Hydrates in the Presence of Polycyclic Aromatic Compounds: Evidence of Ion-Specific Effects

Polycyclic aromatic hydrocarbons (PAHs) have attracted remarkable multidisciplinary attention due to their intriguing π-π stacking configurations, showing enormous opportunity for their use in a variety of advanced applications. To secure progress, detailed knowledge on PAHs' interfacial properties is required. Employing molecular dynamics, we probe the wetting properties of brine droplets (KCl, NaCl, and CaCl₂) on sII methane-ethane hydrate surfaces immersed in various oil solvents. Our simulations show synergistic effects due to the presence of PAHs compounded by ion-specific effects. Our analysis reveals phenomenological correlations between the wetting properties and a combination of the binding free-energy difference and entropy changes upon oil solvation for PAHs at oil/brine and oil/hydrate interfaces. The detailed thermodynamic analysis conducted upon the interactions between PAHs and various interfaces identifies molecular-level mechanisms responsible for wettability alterations, which could be applicable for advancing applications in optics, microfluidics, biotechnology, medicine, as well as hydrate management.

Stick or Spill? Scaling Relationships for the Binding Energies of Adsorbates on Single-Atom Alloy Catalysts

Single-atom alloy catalysts combine catalytically active metal atoms, present as dopants, with the selectivity of coinage metal hosts. Determining whether adsorbates stick at the dopant or spill over onto the host is key to understanding catalytic mechanisms on these materials. Despite a growing body of work, simple descriptors for the prediction of spillover energies (SOEs), i.e., the relative stability of an adsorbate on the dopant versus the host site, are not yet available. Using Density Functional Theory (DFT) calculations on a large set of adsorbates, we identify the dopant charge and the SOE of carbon as suitable descriptors. Combining them into a linear surrogate model, we can reproduce DFT-computed SOEs within 0.06 eV mean absolute error. More importantly, our work provides an intuitive theoretical framework, based on the concepts of electrostatic interactions and covalency, that explains SOE trends and can guide the rational design of future single-atom alloy catalysts.

Observation and Characterization of Dicarbonyls on a RhCu Single-Atom Alloy

Dicarbonyl species are ubiquitous on Rh/oxide catalysts and are known to form on Rh⁺ centers. However, dicarbonyl species have never been directly observed on single-atom alloys (SAAs) where the active site is metallic. Herein, using surface science and theoretical modeling, we provide evidence of dicarbonyl species at isolated Rh sites on a RhCu(100) SAA. This approach not only enables us to directly visualize dicarbonyl species at Rh sites but also demonstrates that the transition between the mono- and dicarbonyl configuration can be achieved by changing surface temperature and CO pressure. Density functional theory calculations further support the mono- and dicarbonyl assignments and provide evidence that these species should be stable on other SAA combinations. Together, these results provide a picture of the structure and energetics of both the mono- and dicarbonyl configurations on the RhCu(100) SAA surface and should aid with IR assignments on SAA nanoparticle catalysts.

Quantum Tunnelling Driven H2 Formation on Graphene

It is commonly believed that it is unfavorable for adsorbed H atoms on carbonaceous surfaces to form H₂ without the help of incident H atoms. Using ring-polymer instanton theory to describe multidimensional tunnelling effects, combined with ab initio electronic structure calculations, we find that these quantum-mechanical simulations reveal a qualitatively different picture. Recombination of adsorbed H atoms, which was believed to be irrelevant at low temperature due to high barriers, is enabled by deep tunnelling, with reaction rates enhanced by tens of orders of magnitude. Furthermore, we identify a new path for H recombination that proceeds via multidimensional tunnelling but would have been predicted to be unfeasible by a simple one-dimensional description of the reaction. The results suggest that hydrogen molecule formation at low temperatures are rather fast processes that should not be ignored in experimental settings and natural environments with graphene, graphite, and other planar carbon segments.

Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status, and challenges

Kinetic Monte Carlo (KMC) simulations in combination with first-principles (1p)-based calculations are rapidly becoming the gold-standard computational framework for bridging the gap between the wide range of length scales and time scales over which heterogeneous catalysis unfolds. 1p-KMC simulations provide accurate insights into reactions over surfaces, a vital step toward the rational design of novel catalysts. In this Perspective, we briefly outline basic principles, computational challenges, successful applications, as well as future directions and opportunities of this promising and ever more popular kinetic modeling approach.

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.

Periodic Trends in Adsorption Energies around Single-Atom Alloy Active Sites

Single-atom alloys (SAAs) make up a special class of alloy surface catalysts that offer well-defined, isolated active sites in a more inert metal host. The dopant sites are generally assumed to have little or no influence on the properties of the host metal, and transport of chemical reactants and products to and from the dopant sites is generally assumed to be facile. Here, by performing density functional theory calculations and surface science experiments, we identify a new physical effect on SAA surfaces, whereby adsorption is destabilized by ≤300 meV on host sites within the perimeter of the reactive dopant site. We identify periodic trends for this behavior and demonstrate a zone of exclusion around the reactive sites for a range of adsorbates and combinations of host and dopant metals. Experiments confirm an increased barrier for diffusion of CO toward the dopant on a RhCu SAA. This effect offers new possibilities for understanding and designing active sites with tunable energetic landscapes surrounding them.

Correlating Antiagglomerant Performance with Gas Hydrate Cohesion

Although inhibiting hydrate formation in hydrocarbon-water systems is paramount in preventing pipe blockage in hydrocarbon transport systems, the molecular mechanisms responsible for antiagglomerant (AA) performance are not completely understood. To better understand why macroscopic performance is affected by apparently small changes in the AA molecular structure, we perform molecular dynamics simulations. We quantify the cohesion energy between two gas hydrate nanoparticles dispersed in liquid hydrocarbons in the presence of different AAs, and we achieve excellent agreement against experimental data obtained at high pressure using the micromechanical force apparatus. This suggests that the proposed simulation approach could provide a screening method for predicting, in silico, the performance of new molecules designed to manage hydrates in flow assurance. Our results suggest that entropy and free energy of solvation of AAs, combined in some cases with the molecular orientation at hydrate-oil interfaces, are descriptors that could be used to predict performance, should the results presented here be reproduced for other systems as well. These insights could help speed up the design of new AAs and guide future experiments.

First-principles design of a single-atom–alloy propane dehydrogenation catalyst

Nanoparticles of rhodium dispersed on metal oxides are generally poor catalysts for alkane dehydrogenation because the reactants bind too strongly to the metal. Hannagan et al. performed first-principle calculations indicating that single rhodium atoms in a copper surface should be stable and selective for conversion of propane to propene and hydrogen. Model studies of single rhodium atoms embedded in a copper (111) surface revealed a very high selectivity to propene and high resistance to the formation of surface carbon that would deactivate the catalyst.

Science , abg8389, this issue p. 1444

Mechanistic insights into carbon-carbon coupling on NiAu and PdAu single-atom alloys

Carbon-carbon coupling is an important step in many catalytic reactions, and performing sp³-sp³ carbon-carbon coupling heterogeneously is particularly challenging. It has been reported that PdAu single-atom alloy (SAA) model catalytic surfaces are able to selectively couple methyl groups, producing ethane from methyl iodide. Herein, we extend this study to NiAu SAAs and find that Ni atoms in Au are active for C-I cleavage and selective sp³-sp³ carbon-carbon coupling to produce ethane. Furthermore, we perform ab initio kinetic Monte Carlo simulations that include the effect of the iodine atom, which was previously considered a bystander species. We find that model NiAu surfaces exhibit a similar chemistry to PdAu, but the reason for the similarity is due to the role the iodine atoms play in terms of blocking the Ni atom active sites. Specifically, on NiAu SAAs, the iodine atoms outcompete the methyl groups for occupancy of the Ni sites leaving the Me groups on Au, while on PdAu SAAs, the binding strengths of methyl groups and iodine atoms at the Pd atom active site are more similar. These simulations shed light on the mechanism of this important sp³-sp³ carbon-carbon coupling chemistry on SAAs. Furthermore, we discuss the effect of the iodine atoms on the reaction energetics and make an analogy between the effect of iodine as an active site blocker on this model heterogeneous catalyst and homogeneous catalysts in which ligands must detach in order for the active site to be accessed by the reactants.

The role of oxygenated species in the catalytic self-coupling of MeOH on O pre-covered Au(111)

The oxidation of alcohols plays a central role in the valorisation of biomass, in particular when performed with a non-toxic oxidant such as O₂. Aerobic oxidation of methanol on gold has attracted attention lately and the main steps of its mechanism have been described experimentally. However, the exact role of O and OH on each elementary step and the effect of the interactions between adsorbates are still not completely understood. Here we investigate the mechanism of methanol oxidation to HCOOCH₃ and CO₂. We use Density Functional Theory (DFT) to assess the energetics of the underlying pathways, and subsequently build lattice kinetic Monte Carlo (kMC) models of increasing complexity, to elucidate the role of different oxygenates. Detailed comparisons of our simulation results with experimental temperature programmed desorption (TPD) spectra enable us to validate the mechanism and identify rate determining steps. Crucially, taking into account dispersion (van der Waals forces) and adsorbate-adsorbate lateral interactions are both important for reproducing the experimental data.

Directing reaction pathways via in situ control of active site geometries in PdAu single-atom alloy catalysts

The atomic scale structure of the active sites in heterogeneous catalysts is central to their reactivity and selectivity. Therefore, understanding active site stability and evolution under different reaction conditions is key to the design of efficient and robust catalysts. Herein we describe theoretical calculations which predict that carbon monoxide can be used to stabilize different active site geometries in bimetallic alloys and then demonstrate experimentally that the same PdAu bimetallic catalyst can be transitioned between a single-atom alloy and a Pd cluster phase. Each state of the catalyst exhibits distinct selectivity for the dehydrogenation of ethanol reaction with the single-atom alloy phase exhibiting high selectivity to acetaldehyde and hydrogen versus a range of products from Pd clusters. First-principles based Monte Carlo calculations explain the origin of this active site ensemble size tuning effect, and this work serves as a demonstration of what should be a general phenomenon that enables in situ control over catalyst selectivity.

Single-Atom Alloys for the Electrochemical Oxygen Reduction Reaction

Single-atom alloys (SAAs) consisting of isolated transition-metal atoms doped in the surface of coinage metal hosts exhibit unique catalytic properties, harnessing the high activity of the dopant metals with the selectivity of the coinage metal hosts. Here we use density functional theory (DFT) to study SAAs comprised of Ni, Pd, Pt, Co and Rh doped into Ag and Au hosts, as candidate electrocatalysts for the oxygen reduction reaction (ORR) in proton-exchange membrane (PEM) fuel-cells. Our calculations reveal that the PdAu SAA exhibits a slightly lower theoretical overpotential, enhanced selectivity for 4-e- ORR, and tolerance to CO-poisoning compared to Pt(111). While the number of active sites of PdAu SAA is lower than that of Pt(111), the aforementioned desirable properties could bring the overall catalytic performance thereof close to that of Pt/C, indicating that the PdAu SAA could be a viable material for electrocatalytic ORR in PEM fuel-cells.

DFT benchmark studies on representative species and poisons of methane steam reforming on Ni(111)

Ni catalysts used in methane steam reforming (MSR) are highly susceptible to poisoning by carbon-based species, which poses a major impediment to the productivity of industrial operations. These species encompass graphitic carbon-like formations that are typically modelled as graphene. First principles-based approaches, such as density functional theory (DFT) calculations, can provide valuable insight into the mechanism of graphene growth in the MSR reaction. It is, however, critical that a DFT model of this reaction can accurately describe the interactions of Ni(111) with the MSR intermediates as well as graphene. In this work, a systematic benchmark study has been carried out to identify a suitable DFT functional for the graphene-MSR system. The binding energies of graphene and important MSR species, as well as the reaction energies of methane dissociation and carbon oxidation, were computed on Ni(111) using GGA functionals, DFT-D and van der Waals density functionals (vdW-DF). It is well-established that the GGA functionals are inadequate for describing graphene-Ni(111) interactions. In the case of vdW-DF, the optPBE-vdW functional predicts the binding energies of graphene and several important MSR species with reasonable accuracy; however, it provides poor estimates of CO and O binding energies. Among the DFT-D functionals, PBE-D3 and PBE-dDsC have been found to exhibit acceptable accuracy for graphene and most MSR species (excluding adsorbed CO), and therefore, both functionals are promising for elucidating carbon-based catalytic poisoning in the MSR reaction. Overall, no single DFT functional could estimate the binding energies of all the species with equally high accuracy.

The catalytic decomposition of nitrous oxide and the NO + CO reaction over Ni/Cu dilute and single atom alloy surfaces: first-principles microkinetic modelling

Density functional theory calculations and microkinetic modelling reveal that well-engineered Ni/Cu dilute alloys are promising for the catalytic reduction of NO by CO.

Controlling Hydrocarbon (De)Hydrogenation Pathways with Bifunctional PtCu Single-Atom Alloys

The conversions of surface-bound alkyl groups to alkanes and alkenes are important steps in many heterogeneously catalyzed reactions. On the one hand, while Pt is ubiquitous in industry because of its high activity toward C-H activation, many Pt-based catalysts tend to overbind reactive intermediates, which leads to deactivation by carbon deposition and coke formation. On the other hand, Cu binds intermediates more weakly than Pt, but activation barriers tend to be higher on Cu. We examine the reactivity of ethyl, the simplest alkyl group that can undergo hydrogenation and dehydrogenation via β-elimination, and show that isolated Pt atoms in Cu enable low-temperature hydrogenation of ethyl, unseen on Cu, while avoiding the decomposition pathways on pure Pt that lead to coking. Furthermore, we confirm the predictions of our theoretical model and experimentally demonstrate that the selectivity of ethyl (de)hydrogenation can be controlled by changing the surface coverage of hydrogen.

A Caching Scheme To Accelerate Kinetic Monte Carlo Simulations of Catalytic Reactions

Kinetic Monte Carlo (KMC) simulations have been instrumental in advancing our fundamental understanding of heterogeneously catalyzed reactions, with particular emphasis on structure sensitivity, ensemble effects, and the interplay between adlayer structure and adsorbate-adsorbate lateral interactions in shaping the observed kinetics. Yet, the computational cost of KMC remains high, thereby motivating the development of acceleration schemes that would improve the simulation efficiency. We present an exact such scheme, which implements a caching algorithm along with shared-memory parallelization to improve the computational performance of simulations incorporating long-range adsorbate-adsorbate lateral interactions. This scheme is based on caching information about the energetic interaction patterns associated with the products of each possible lattice process (adsorption, desorption, reaction etc.). Thus, every time a reaction occurs ("ongoing reaction"), it enables fast updates of the rate constants of "affected reactions", i.e., possible reactions in the region of influence of the "ongoing reaction". Benchmarks on KMC simulations of NO_x oxidation/reduction, yielded acceleration factors of up to 20, when comparing single-thread runs without caching to runs on 16 threads with caching, for simulations with a cluster expansion Hamiltonian that incorporates up to 8th-nearest-neighbor interactions.

A DFT and KMC based study on the mechanism of the water gas shift reaction on the Pd(100) surface

We present a combined density functional theory (DFT) and Kinetic Monte Carlo (KMC) study of the water gas shift (WGS) reaction on the Pd(100) surface. We propose a mechanism comprising both the redox and the associative pathways for the WGS within a single framework, which consists of seven core elementary steps, which in turn involve splitting of a water molecule followed by the production of an H-atom and an OH-species on the Pd(100) surface. In the following steps, these intermediates then recombine with each other and with CO leading to the evolution of CO₂, and H₂. Seven other elementary steps, involving the diffusion and adsorption of the surface intermediate species are also considered for a complete description of the mechanism. The geometrical and electronic properties of each of the reactants, products, and the transition states of the core elementary steps are presented. We also discuss the analysis of Bader charges and spin densities for the reactants, transition states and the products of these elementary steps. Our study indicates that the WGS reaction progresses simultaneously via the direct oxidation and the carboxyl paths on the Pd(100) surface.

On the behaviour of structure-sensitive reactions on single atom and dilute alloy surfaces

Typically structure sensitive dissociation reactions exhibit reduced structure-sensitivity when taking place over low-index single atom alloy surfaces.

Quantifying Pore Width Effects on Diffusivity via a Novel 3D Stochastic Approach with Input from Atomistic Molecular Dynamics Simulations

The increased production of unconventional hydrocarbons emphasizes the need to understand the transport of fluids through narrow pores. Although it is well-known that confinement affects fluids structure and transport, it is not yet possible to quantitatively predict properties such as diffusivity as a function of pore width in the range of 1-50 nm. Such pores are commonly found not only in shale rocks but also in a wide range of engineering materials, including catalysts. We propose here a novel and computationally efficient methodology to obtain accurate diffusion coefficient predictions as a function of pore width for pores carved out of common materials, such as silica, alumina, magnesium oxide, calcite, and muscovite. We implement atomistic molecular dynamics (MD) simulations to quantify fluid structure and transport within 5 nm-wide pores, with particular focus on the diffusion coefficient within different pore regions. We then use these data as input to a bespoke stochastic kinetic Monte Carlo (KMC) model, developed to predict fluid transport in mesopores. The KMC model is used to extrapolate the fluid diffusivity for pores of increasing width. We validate the approach against atomistic MD simulation results obtained for wider pores. When applied to supercritical methane in slit-shaped pores, our methodology yields data within 10% of the atomistic simulation results, with significant savings in computational time. The proposed methodology, which combines the advantages of MD and KMC simulations, is used to generate a digital library for the diffusivity of gases as a function of pore chemistry and pore width and could be relevant for a number of applications, from the prediction of hydrocarbon transport in shale rocks to the optimization of catalysts, when surface-fluid interactions impact transport.

Efficient and selective carbon–carbon coupling on coke-resistant PdAu single-atom alloys

We demonstrate that PdAu single-atom alloy model catalysts offer a heterogeneous route to selective Würtz-type C–C coupling.

Lonely Atoms with Special Gifts: Breaking Linear Scaling Relationships in Heterogeneous Catalysis with Single-Atom Alloys

We discuss a simple yet effective strategy for escaping traditional linear scaling relations in heterogeneous catalysis with highly dilute bimetallic alloys known as single-atom alloys (SAAs). These systems, in which a reactive metal is atomically dispersed in a less reactive host, were first demonstrated with the techniques of surface science to be active and selective for hydrogenation reactions. Informed by these early results, PdCu and PtCu SAA nanoparticle hydrogenation catalysts were shown to work under industrially relevant conditions. To efficiently survey the many potential metal combinations and reactions, simulation is crucial for making predictions about reactivity and guiding experimental focus on the most promising candidate materials. This recent work reveals that the high surface chemical heterogeneity of SAAs can result in significant deviations from Brønsted-Evans-Polanyi scaling relationships for many key reaction steps. These recent insights into SAAs and their ability to break linear scaling relations motivate discovery of novel alloy catalysts.

Beyond mean-field approximations for accurate and computationally efficient models of on-lattice chemical kinetics

Modeling the kinetics of surface catalyzed reactions is essential for the design of reactors and chemical processes. The majority of microkinetic models employ mean-field approximations, which lead to an approximate description of catalytic kinetics by assuming spatially uncorrelated adsorbates. On the other hand, kinetic Monte Carlo (KMC) methods provide a discrete-space continuous-time stochastic formulation that enables an accurate treatment of spatial correlations in the adlayer, but at a significant computation cost. In this work, we use the so-called cluster mean-field approach to develop higher order approximations that systematically increase the accuracy of kinetic models by treating spatial correlations at a progressively higher level of detail. We further demonstrate our approach on a reduced model for NO oxidation incorporating first nearest-neighbor lateral interactions and construct a sequence of approximations of increasingly higher accuracy, which we compare with KMC and mean-field. The latter is found to perform rather poorly, overestimating the turnover frequency by several orders of magnitude for this system. On the other hand, our approximations, while more computationally intense than the traditional mean-field treatment, still achieve tremendous computational savings compared to KMC simulations, thereby opening the way for employing them in multiscale modeling frameworks.

A kinetic Monte Carlo approach to study fluid transport in pore networks

The mechanism of fluid migration in porous networks continues to attract great interest. Darcy's law (phenomenological continuum theory), which is often used to describe macroscopically fluid flow through a porous material, is thought to fail in nano-channels. Transport through heterogeneous and anisotropic systems, characterized by a broad distribution of pores, occurs via a contribution of different transport mechanisms, all of which need to be accounted for. The situation is likely more complicated when immiscible fluid mixtures are present. To generalize the study of fluid transport through a porous network, we developed a stochastic kinetic Monte Carlo (KMC) model. In our lattice model, the pore network is represented as a set of connected finite volumes (voxels), and transport is simulated as a random walk of molecules, which "hop" from voxel to voxel. We simulated fluid transport along an effectively 1D pore and we compared the results to those expected by solving analytically the diffusion equation. The KMC model was then implemented to quantify the transport of methane through hydrated micropores, in which case atomistic molecular dynamic simulation results were reproduced. The model was then used to study flow through pore networks, where it was able to quantify the effect of the pore length and the effect of the network's connectivity. The results are consistent with experiments but also provide additional physical insights. Extension of the model will be useful to better understand fluid transport in shale rocks.