Ensemble representation of catalytic interfaces: soloists, orchestras, and everything in-between

GOCIA: a grand canonical global optimizer for clusters, interfaces, and adsorbates

Restructuring of surfaces and interfaces plays a key role in the activation and/or deactivation of a wide spectrum of heterogeneous catalysts and functional materials. The statistical ensemble representation can provide unique atomistic insights into this fluxional and metastable realm, but constructing the ensemble is very challenging, especially for the systems with off-stoichiometric reconstruction and varying coverage of mixed adsorbates. Here, we report GOCIA, a versatile global optimizer for exploring the chemical space of these systems. It features the grand canonical genetic algorithm (GCGA), which bases the target function on the grand potential and evolves across the compositional space, as well as many useful functionalities, with implementation details explained. GOCIA has been applied to various systems in catalysis, from clusters to surfaces and from thermal to electrocatalysis.

Cu-Supported ZnO under Conditions of CO2 Reduction to Methanol: Why 0.2 ML Coverage?

By hydrogenating carbon dioxide to value-added products such as methanol, heterogeneous catalysts can lower greenhouse gas emissions and generate alternative liquid fuels. The most common commercial catalyst for the reduction of CO2 to methanol is Cu/ZnO/Al2O3, where ZnO improves conversion and selectivity toward methanol. The structure of this catalyst is thought to be Zn oxy(hydroxyl) overlayers on the nanometer scale on Cu. In the presence of CO2 and H2 under reaction conditions, the Cu substrate itself can be restructured and/or partially oxidized at its interface with ZnO, or the Zn might be reduced, possibly completely to a CuZn alloy, making the exact structure and stoichiometry of the active site a topic of active debate. In this study, we examine Zn3 clusters on Cu(100) and Cu(111), as a subnano model of the catalyst. We use a grand canonical genetic algorithm to sample the system structure and stoichiometry under catalytic conditions: T of 550 K, initial partial pressures of H2 of 4.5 atm and CO2 of 0.5 atm, and 1% conversion. We uncover a strong dependence of the catalyst stoichiometry on the surface coverage. At the optimal 0.2 ML surface coverage, chains of Zn(OH) form on both Cu surfaces. On Cu(100), the catalyst has many thermally accessible metastable minima, whereas on Cu(111), it does not. No oxidation or reconstruction of the Cu is found. However, at a lower coverage of Zn, Zn3 clusters take on a metallic form on Cu(100), and slightly oxidized Zn3O on Cu(111), while the surface uptakes H to form a variety of low hydrides of Cu. We thus hypothesize that the 0.2 ML Zn coverage is optimal, as found experimentally, because of the stronger yet incomplete oxidation afforded by Zn at this coverage.

Entropy in catalyst dynamics under confinement

Entropy during the dynamic structural evolution of catalysts has a non-trivial influence on chemical reactions. Confinement significantly affects the catalyst dynamics and thus impacts the reactivity. However, a full understanding has not been clearly established. To investigate catalyst dynamics under confinement, we utilize the active learning scheme to effectively train machine learning potentials for computing free energies of catalytic reactions. The scheme enables us to compute the reaction free energies and entropies of O2 dissociation on Pt clusters with different sizes confined inside a carbon nanotube (CNT) at the timescale of tens of nanoseconds while keeping ab initio accuracy. We observe an entropic effect owing to liquid-to-solid phase transitions of clusters at finite temperatures. More importantly, the confinement effect enhances the structural dynamics of the cluster and leads to a lower melting temperature than those of the bare cluster and cluster outside the CNT, consequently facilitating the reaction to occur at lower temperatures and preventing the catalyst from forming unfavorable oxides. Our work reveals the important influence of confinement on structural dynamics, providing useful insight into entropy in dynamic catalysis.

Emerging Atomistic Modeling Methods for Heterogeneous Electrocatalysis

Heterogeneous electrocatalysis lies at the center of various technologies that could help enable a sustainable future. However, its complexity makes it challenging to accurately and efficiently model at an atomic level. Here, we review emerging atomistic methods to simulate the electrocatalytic interface with special attention devoted to the components/effects that have been challenging to model, such as solvation, electrolyte ions, electrode potential, reaction kinetics, and pH. Additionally, we review relevant computational spectroscopy methods. Then, we showcase several examples of applying these methods to understand and design catalysts relevant to green hydrogen. We also offer experimental views on how to bridge the gap between theory and experiments. Finally, we provide some perspectives on opportunities to advance the field.

Machine Learning Molecular Dynamics Shows Anomalous Entropic Effect on Catalysis through Surface Pre-melting of Nanoclusters

Due to the superior catalytic activity and efficient utilization of noble metals, nanocatalysts are extensively used in the modern industrial production of chemicals. The surface structures of these materials are significantly influenced by reactive adsorbates, leading to dynamic behavior under experimental conditions. The dynamic nature poses significant challenges in studying the structure-activity relations of catalysts. Herein, we unveil an anomalous entropic effect on catalysis via surface pre-melting of nanoclusters through machine learning accelerated molecular dynamics and free energy calculation. We find that due to the pre-melting of shell atoms, there exists a non-linear variation in the catalytic activity of the nanoclusters with temperature. Consequently, two notable changes in catalyst activity occur at the respective temperatures of melting for the shell and core atoms. We further study the nanoclusters with surface point defects, i.e. vacancy and ad-atom, and observe significant decrease in the surface melting temperatures of the nanoclusters, enabling the reaction to take place under more favorable and milder conditions. These findings not only provide novel insights into dynamic catalysis of nanoclusters but also offer new understanding of the role of point defects in catalytic processes.

H and CO Co-Induced Roughening of Cu Surface in CO2 Electroreduction Conditions

The dynamic restructuring of Cu has been observed under electrochemical conditions, and it has been hypothesized to underlie the unique reactivity of Cu toward CO2 electroreduction. Roughening is one of the key surface phenomena for Cu activation, whereby numerous atomic vacancies and adatoms form. However, the atomic structure of such surface motifs in the presence of relevant adsorbates has remained elusive. Here, we explore the chemical space of Cu surface restructuring under coverage of CO and H in realistic electroreduction conditions, by combining grand canonical DFT and global optimization techniques, from which we construct a potential-dependent grand canonical ensemble representation. The regime of intermediate and mixed CO and H coverage─where structures exhibit some elevated surface Cu─is thermodynamically unfavorable yet kinetically inevitable. Therefore, we develop a quasi-kinetic Monte Carlo simulation to track the system's evolution during a simulated cathodic scan. We reveal the evolution path of the system across coverage space and identify the accessible metastable structures formed along the way. Chemical bonding analysis is performed on the metastable structures with elevated Cu*CO species to understand their formation mechanism. By molecular dynamics simulations and free energy calculations, the surface chemistry of the Cu*CO species is explored, and we identify plausible mechanisms via which the Cu*CO species may diffuse or dimerize. This work provides rich atomistic insights into the phenomenon of surface roughening and the structure of involved species. It also features generalizable methods to explore the chemical space of restructuring surfaces with mixed adsorbates and their nonequilibrium evolution.

Surface phase diagrams from nested sampling

Studies in atomic-scale modeling of surface phase equilibria often focus on temperatures near zero Kelvin due to the challenges in calculating the free energy of surfaces at finite temperatures. The Bayesian-inference-based nested sampling (NS) algorithm allows for modeling phase equilibria at arbitrary temperatures by directly and efficiently calculating the partition function, whose relationship with free energy is well known. This work extends NS to calculate adsorbate phase diagrams, incorporating all relevant configurational contributions to the free energy. We apply NS to the adsorption of Lennard-Jones (LJ) gas particles on low-index and vicinal LJ solid surfaces and construct the canonical partition function from these recorded energies to calculate ensemble averages of thermodynamic properties, such as the constant-volume heat capacity and order parameters that characterize the structure of adsorbate phases. Key results include determining the nature of phase transitions of adsorbed LJ particles on flat and stepped LJ surfaces, which typically feature an enthalpy-driven condensation at higher temperatures and an entropy-driven reordering process at lower temperatures, and the effect of surface geometry on the presence of triple points in the phase diagrams. Overall, we demonstrate the ability and potential of NS for surface modeling.

Operando Electron Microscopy of Catalysts: The Missing Cornerstone in Heterogeneous Catalysis Research?

Heterogeneous catalysis in thermal gas-phase and electrochemical liquid-phase chemical conversion plays an important role in our modern energy landscape. However, many of the structural features that drive efficient chemical energy conversion are still unknown. These features are, in general, highly distinct on the local scale and lack translational symmetry, and thus, they are difficult to capture without the required spatial and temporal resolution. Correlating these structures to their function will, conversely, allow us to disentangle irrelevant and relevant features, explore the entanglement of different local structures, and provide us with the necessary understanding to tailor novel catalyst systems with improved productivity. This critical review provides a summary of the still immature field of operando electron microscopy for thermal gas-phase and electrochemical liquid-phase reactions. It focuses on the complexity of investigating catalytic reactions and catalysts, progress in the field, and analysis. The forthcoming advances are discussed in view of correlative techniques, artificial intelligence in analysis, and novel reactor designs.

Protein Dynamics and Enzymatic Catalysis

This Perspective presents a review of our work and that of others in the highly controversial topic of the coupling of protein dynamics to reaction in enzymes. We have been involved in studying this topic for many years. Thus, this perspective will naturally present our own views, but it also is designed to present an overview of the variety of viewpoints of this topic, both experimental and theoretical. This is obviously a large and contentious topic.

Exploring the Potential Energy Surface of Pt6 Sub-Nano Clusters Deposited over Graphene

Catalytic systems based on sub-nanoclusters deposited over different supports are promising for very relevant chemical transformations such as many electrocatalytic processes as the ORR. These systems have been demonstrated to be very fluxional, as they are able to change shape and interconvert between each other either alone or in the presence of adsorbates. In addition, an accurate representation of their catalytic activity requires the consideration of ensemble effects and not a single structure alone. In this sense, a reliable theoretical methodology should assure an accurate and extensive exploration of the potential energy surface to include all the relevant structures and with correct relative energies. In this context, we applied DFT in conjunction with global optimization techniques to obtain and analyze the characteristics of the many local minima of Pt₆ sub-nanoclusters over a carbon-based support (graphene)-a system with electrocatalytic relevance. We also analyzed the magnetism and the charge transfer between the clusters and the support and paid special attention to the dependence of dispersion effects on the ensemble characteristics. We found that the ensembles computed with and without dispersion corrections are qualitatively similar, especially for the lowest-in-energy clusters, which we attribute to a (mainly) covalent binding to the surface. However, there are some significant variations in the relative stability of some clusters, which would significantly affect their population in the ensemble composition.