Kinetic Monte Carlo simulations of the water gas shift reaction on Cu(1 1 1) from density functional theory based calculations

Comprehensive Density Functional and Kinetic Monte Carlo Study of CO2 Hydrogenation on a Well-Defined Ni/CeO2 Model Catalyst: Role of Eley-Rideal Reactions

A detailed multiscale study of the mechanism of CO2 hydrogenation on a well-defined Ni/CeO2 model catalyst is reported that couples periodic density functional theory (DFT) calculations with kinetic Monte Carlo (kMC) simulations. The study includes an analysis of the role of Eley-Rideal elementary steps for the water formation step, which are usually neglected on the overall picture of the mechanism, catalytic activity, and selectivity. The DFT calculations for the chosen model consisting of a Ni4 cluster supported on CeO2 (111) show large enough adsorption energies along with low energy barriers that suggest this catalyst to be a good option for high selective CO2 methanation. The kMC simulations results show a synergic effect between the two 3-fold hollow sites of the supported Ni4 cluster with some elementary reactions dominant in one site, while other reactions prefer the another, nearly equivalent site. This effect is even more evident for the simulations explicitly including Eley-Rideal steps. The kMC simulations reveal that CO is formed via the dissociative pathway of the reverse water-gas shift reaction, while methane is formed via a CO2 → CO → HCO → CH → CH2 → CH3 → CH4 mechanism. Overall, our results show the importance of including the Eley-Rideal reactions and point to small Ni clusters supported on the CeO2 (111) surface as potential good catalysts for high selective CO2 methanation under mild conditions, while very active and selective toward CO formation at higher temperatures.

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.

A density functional theory study of a water gas shift reaction on Ag(111): potassium effect

Density functional theory (DFT) calculations are executed to investigate the effect of a potassium (K) promoter on the activity of the water gas shift reaction (WGSR) over an Ag(111) surface.

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.

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.

First-principles-based kinetic Monte Carlo simulations of CO oxidation on catalytic Au(110) and Ag(110) surfaces

At the core of the development of more efficient and reliable fuel cells (FCs), there are several essential chemical reactions, namely carbon monoxide (CO) oxidation. This reaction is a keystone in the cleaning of hydrogen fuel used in fuel cells due to strong poisoning by this species of the platinum catalyst used in these devices. The present work aims to provide insight regarding the activation of CO oxidation by gold or silver microfacets possessing low coordinated atoms. To achieve this, density functional theory (DFT) quantum calculations, which determined two competing reaction pathways for CO oxidation, i.e., by molecularly adsorbed oxygen, and by dissociated oxygen, are combined with first-principles kinetic Monte Carlo (1p-kMC) simulations, which employed the resulting DFT parameters in order to address the effect of temperature and partial pressures and the interplay of the elementary reaction events. The use of 1p-kMC is a step further from available works regarding the CO oxidation on gold- and silver-based catalysts for cleansing of hydrogen that is used as a fuel in FCs. Indeed, this research contributes to the conclusion that CO oxidation should preferentially occur on silver microfacets, while the obtained turnover frequencies (TOFs) reinforced such a conclusion.

Comparison of Queueing Data-Structures for Kinetic Monte Carlo Simulations of Heterogeneous Catalysts

On-lattice kinetic Monte Carlo (KMC) is a computational method used to simulate (among others) physicochemical processes on catalytic surfaces. The KMC algorithm propagates the system through discrete configurations by selecting (with the use of random numbers) the next elementary process to be simulated, e.g., adsorption, desorption, diffusion, or reaction. An implementation of such a selection procedure is the first-reaction method in which all realizable elementary processes are identified and assigned a random occurrence time based on their rate constant. The next event to be executed will then be the one with the minimum interarrival time. Thus, a fast and efficient algorithm for selecting the most imminent process and performing all of the necessary updates on the list of realizable processes post execution is of great importance. In the current work, we implement five data-structures to handle the elementary process queue during a KMC run: an unsorted list, a binary heap, a pairing heap, a one-way skip list, and finally, a novel two-way skip list with a mapping array specialized for KMC simulations. We also investigate the effect of compiler optimizations on the performance of these data-structures on three benchmark models, capturing CO oxidation, a simplified water gas shift mechanism, and a temperature-programmed desorption run. Excluding the least efficient and impractical for large-problems unsorted list, we observe a 3× speedup of the binary or pairing heaps (most efficient) compared to the one-way skip list (least efficient). Compiler optimizations deliver a speedup of up to 1.8×. These benchmarks provide valuable insight into the importance of, often-overlooked, implementation-related aspects of KMC simulations, such as the queueing data-structures. Our results could be particularly useful in guiding the choice of data-structures and algorithms that would minimize the computational cost of large-scale simulations.

How Size Matters: Electronic, Cooperative, and Geometric Effect in Perovskite-Supported Copper Catalysts for CO2 Reduction

In heterogeneous catalysis, bifunctional catalysts often outperform one-component catalysts. The activity is also strongly influenced by the morphology, size, and distribution of catalytic particles. For CO2 hydrogenation, the size of the active copper area on top of the SrTiO3 perovskite catalyst support can affect the activity, selectivity, and stability. Here, a detailed theoretical study of the effect of bifunctionality on an important chemical CO2 transformation reaction, the reverse water gas shift (RWGS) reaction, is presented. Using density functional theory computation results for the RWGS pathway on three surfaces, namely, Cu(111), SrTiO3, and the Cu/SrTiO3 interface between both solid phases, we construct the energy landscape of the reaction. The adsorbate-adsorbate lateral interactions are taken into account for catalytic surfaces, which show a sufficient intermediate coverage. The mechanism, combining all three surfaces, is used in mesoscale kinetic Monte Carlo simulations to study the turnover and yield for CO production as a function of particle size. It is shown that the reaction proceeds faster at the interface. However, including copper and the support sites in addition to the interface accelerates the conversion even further, showing that the bifunctionality of the catalyst manifests in a more complex interplay between the phases than just the interface effect, such as the hydrogen spillover. We identify three distinct effects, the electronic, cooperative, and geometric effects, and show that the surrounded smaller Cu features on the set of supporting SrTiO3 show a higher CO formation rate, resulting in a decreasing RWGS model trend with the increasing Cu island size. The findings are in parallel with experiments, showing that they explain the previously observed phenomena and confirming the size sensitivity for the catalytic RWGS reaction.

Kinetic Monte-Carlo Simulation of Methane Steam Reforming over a Nickel Surface

A kinetic Monte-Carlo model was developed in order to simulate the methane steam reforming and kinetic behavior of this reaction. There were 34 elementary step reactions that were used, based on the Langmuir–Hinshelwood mechanism, over a nickel catalyst. The simulation was investigated at a mole fraction of methane between 0.1 and 0.9, temperature of 600 to 1123 K, and total pressure of up to 40 bar. The simulated results were collected at a steady state and were compared with the previously reported experiments. The fractional coverages of the adsorbed species and the production rates of H2, CO, and CO2 were evaluated, and the effects of the reaction temperature, feed concentration, and total pressure of reactants were also investigated. The simulation results showed a similar trend with previous experimental results, and suggested the appropriate conditions for this reaction, which were a total pressure of 10 bar, with the mole fraction of methane in a range of 0.4–0.5.

Implicit solvent effects in the determination of Brønsted–Evans–Polanyi relationships for heterogeneously catalyzed reactions

The Brønsted–Evans–Polanyi relationship derived for the water dissociation reaction within an implicit solvent approach is similar to that without such effects.

H2 Thermal Desorption Spectra on Pt(111): A Density Functional Theory and Kinetic Monte Carlo Simulation Study

Theoretical investigation of the static and kinetic behaviors of H and H2 on metal surface plays a key role in the development of hydrogenation catalysts and new materials with high H2 storage capacity. Based on the density functional theory (DFT) calculation of H and H2 adsorption on Pt(111), H(a) adatom strongly interacts with surface Pt; while H2 weakly adsorbs on Pt(111). H(a) adatoms stably occupy the face-centered cubic sites on Pt(111) which agrees with the experimental LERS observations. By using kinetic Monte Carlo (kMC) simulation, the qualitative effects of the kinetic parameters on the H2 TDS spectra indicate that the H2 desorption peaks shift to the low temperature with increasing pre-exponential factor and decreasing desorption barrier. Simultaneously, the desorption peaks shift downwards and broaden to two peaks with the increase of the lateral interaction energy among H(a) adatoms. Using the kMC simulation based on DFT calculation, the predicted H2 TDS spectra are well consistent with the experimental ones. It unanimously proves that the two peaks of TDS spectra are derived from the lateral interactions among H(a). This work provides the intrinsic kinetics of H(a) and H2 on Pt(111) at an atomic level, and gives insight into the development of hydrogenation catalysts.

Highly active Au/δ-MoC and Au/β-Mo2C catalysts for the low-temperature water gas shift reaction: effects of the carbide metal/carbon ratio on the catalyst performance

Water gas shift reaction catalyzed by Mo carbides surfaces and on Au supported thereon is studied by experiments and computational methods.

Unravelling the mechanisms of CO2 hydrogenation to methanol on Cu-based catalysts using first-principles multiscale modelling and experiments

Multi-scale modelling of various copper-based catalysts showed how and why different catalysts perform in methanol synthesis via carbon dioxide hydrogenation.

Unexpectedly large impact of van der Waals interactions on the description of heterogeneously catalyzed reactions: the water gas shift reaction on Cu(321) as a case example

The molecular mechanisms of the water gas shift reaction on Cu(321) have been chosen to investigate the effect of dispersion terms on the description of the energy profile and reaction rates.

Surface carbon species formation from ethylene decomposition on Pd(100): a first-principles-based kinetic Monte Carlo study

Based on the activation barriers and reaction energies from periodic density functional calculations, we conducted kinetic Monte Carlo (kMC) simulations of surface carbon species formation from ethylene decomposition on a Pd(100) surface.