Perspective: On the active site model in computational catalyst screening

Single-atom and cluster catalysts for thermocatalytic ammonia synthesis at mild conditions

Ammonia (NH3) is closely related to the fields of food and energy that humans depend on. The exploitation of advanced catalysts for NH3 synthesis has been a research hotspot for more than one hundred years. Previous studies have shown that the Ru B5 sites (step sites on the Ru (0001) surface uniquely arranged with five Ru atoms) and Fe C7 sites (iron atoms with seven nearest neighbors) over nanoparticle catalysts are highly reactive for N2-to-NH3 conversion. In recent years, single-atom and cluster catalysts, where the B5 sites and C7 sites are absent, have emerged as promising catalysts for efficient NH3 synthesis. In this review, we focus on the recent advances in single-atom and cluster catalysts, including single-atom catalysts (SACs), single-cluster catalysts (SCCs), and bimetallic-cluster catalysts (BCCs), for thermocatalytic NH3 synthesis at mild conditions. In addition, we discussed and summarized the unique structural properties and reaction performance as well as reaction mechanisms over single-atom and cluster catalysts in comparison with traditional nanoparticle catalysts. Finally, the challenges and prospects in the rational design of efficient single-atom and cluster catalysts for NH3 synthesis were provided.

Invariant Molecular Representations for Heterogeneous Catalysis

Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used. This study introduces a novel machine learning (ML) method to learn adsorption energies from multiple DFT functionals by using invariant molecular representations (IMRs). To do this, we first extract molecular fingerprints for the reaction intermediates and later use a Siamese-neural-network-based training strategy to learn invariant molecular representations or the IMR across all available functionals. Our Siamese network-based representations demonstrate superior performance in predicting adsorption energies compared with other molecular representations. Notably, when considering mean absolute values of adsorption energies as 0.43 eV (PBE-D3), 0.46 eV (BEEF-vdW), 0.81 eV (RPBE), and 0.37 eV (scan+rVV10), our IMR method has achieved the lowest mean absolute errors (MAEs) of 0.18 0.10, 0.16, and 0.18 eV, respectively. These results emphasize the superior predictive capacity of our Siamese network-based representations. The empirical findings in this study illuminate the efficacy, robustness, and dependability of our proposed ML paradigm in predicting adsorption energies, specifically for propane dehydrogenation on a platinum catalyst surface.

The highest oxidation state observed in graphene-supported sub-nanometer iron oxide clusters

Size-selected iron oxide nanoclusters are outstanding candidates for technological-oriented applications due to their high efficiency-to-cost ratio. However, despite many theoretical studies, experimental works on their oxidation mechanism are still limited to gas-phase clusters. Herein we investigate the oxidation of graphene-supported size-selected Fe_n clusters by means of high-resolution X-ray Photoelectron Spectroscopy. We show a dependency of the core electron Fe 2p_3/2 binding energy of metallic and oxidized clusters on the cluster size. Binding energies are also linked to chemical reactivity through the asymmetry parameter which is related to electron density of states at the Fermi energy. Upon oxidation, iron atoms in clusters reach the oxidation state Fe(II) and the absence of other oxidation states indicates a Fe-to-O ratio close to 1:1, in agreement with previous theoretical calculations and gas-phase experiments. Such knowledge can provide a basis for a better understanding of the behavior of iron oxide nanoclusters as supported catalysts.

N2-to-NH3 conversion by excess electrons trapped in point vacancies on 5f-element dioxide surfaces

Ammonia (NH₃) is one of the basic chemicals in artificial fertilizers and a promising carbon-free energy storage carrier. Its industrial synthesis is typically realized via the Haber-Bosch process using traditional iron-based catalysts. Developing advanced catalysts that can reduce the N₂ activation barrier and make NH₃ synthesis more efficient is a long-term goal in the field. Most heterogeneous catalysts for N₂-to-NH₃ conversion are multicomponent systems with singly dispersed metal clusters on supporting materials to activate N₂ and H₂ molecules. Herein, we report single-component heterogeneous catalysts based on 5f actinide dioxide surfaces (ThO₂ and UO₂) with oxygen vacancies for N₂-to-NH₃ conversion. The reaction cycle we propose is enabled by a dual-site mechanism, where N₂ and H₂ can be activated at different vacancy sites on the same surface; NH₃ is subsequently formed by H^- migration on the surface via associative pathways. Oxygen vacancies recover to their initial states after the release of two molecules of NH₃, making it possible for the catalytic cycle to continue. Our work demonstrates the catalytic activities of oxygen vacancies on 5f actinide dioxide surfaces for N₂ activation, which may inspire the search for highly efficient, single-component catalysts that are easy to synthesize and control for NH₃ conversion.

Engineering single-atom catalysts toward biomedical applications

Due to inherent structural defects, common nanocatalysts always display limited catalytic activity and selectivity, making it practically difficult for them to replace natural enzymes in a broad scope of biologically important applications. By decreasing the size of the nanocatalysts, their catalytic activity and selectivity will be substantially improved. Guided by this concept, the advances of nanocatalysts now enter an era of atomic-level precise control. Single-atom catalysts (denoted as SACs), characterized by atomically dispersed active sites, strikingly show utmost atomic utilization, precisely located metal centers, unique metal-support interactions and identical coordination environments. Such advantages of SACs drastically boost the specific activity per metal atom, and thus provide great potential for achieving superior catalytic activity and selectivity to functionally mimic or even outperform natural enzymes of interest. Although the size of the catalysts does matter, it is not clear whether the guideline of "the smaller, the better" is still correct for developing catalysts at the single-atom scale. Thus, it is clearly a new, urgent issue to address before further extending SACs into biomedical applications, representing an important branch of nanomedicine. This review begins by providing an overview of recent advances of synthesis strategies of SACs, which serve as a basis for the discussion of emerging achievements in improving the enzyme-like catalytic properties at an atomic level. Then, we carefully compare the structures and functions of catalysts at various scales from nanoparticles, nanoclusters, and few-atom clusters to single atoms. Contrary to conventional wisdom, SACs are not the most catalytically active catalysts in specific reactions, especially those requiring multi-site auxiliary activities. After that, we highlight the unique roles of SACs toward biomedical applications. To appreciate these advances, the challenges and prospects in rapidly growing studies of SACs-related catalytic nanomedicine are also discussed in this review.

Improving the intrinsic activity of electrocatalysts for sustainable energy conversion: where are we and where can we go?

As we are in the midst of a climate crisis, there is an urgent need to transition to the sustainable production of fuels and chemicals. A promising strategy towards this transition is to use renewable energy for the electrochemical conversion of abundant molecules present in the earth's atmosphere such as H₂O, O₂, N₂ and CO₂, to synthetic fuels and chemicals. A cornerstone to this strategy is the development of earth abundant electrocatalysts with high intrinsic activity towards the desired products. In this perspective, we discuss the importance and challenges involved in the estimation of intrinsic activity both from the experimental and theoretical front. Through a thorough analysis of published data, we find that only modest improvements in intrinsic activity of electrocatalysts have been achieved in the past two decades which necessitates the need for a paradigm shift in electrocatalyst design. To this end, we highlight opportunities offered by tuning three components of the electrochemical environment: cations, buffering anions and the electrolyte pH. These components can significantly alter catalytic activity as demonstrated using several examples, and bring us a step closer towards complete system level optimization of electrochemical routes to sustainable energy conversion.

Implicit Solvation Methods for Catalysis at Electrified Interfaces

Implicit solvation is an effective, highly coarse-grained approach in atomic-scale simulations to account for a surrounding liquid electrolyte on the level of a continuous polarizable medium. Originating in molecular chemistry with finite solutes, implicit solvation techniques are now increasingly used in the context of first-principles modeling of electrochemistry and electrocatalysis at extended (often metallic) electrodes. The prevalent ansatz to model the latter electrodes and the reactive surface chemistry at them through slabs in periodic boundary condition supercells brings its specific challenges. Foremost this concerns the difficulty of describing the entire double layer forming at the electrified solid–liquid interface (SLI) within supercell sizes tractable by commonly employed density functional theory (DFT). We review liquid solvation methodology from this specific application angle, highlighting in particular its use in the widespread ab initio thermodynamics approach to surface catalysis. Notably, implicit solvation can be employed to mimic a polarization of the electrode’s electronic density under the applied potential and the concomitant capacitive charging of the entire double layer beyond the limitations of the employed DFT supercell. Most critical for continuing advances of this effective methodology for the SLI context is the lack of pertinent (experimental or high-level theoretical) reference data needed for parametrization.

Adsorption Enthalpies for Catalysis Modeling through Machine-Learned Descriptors

Heterogeneous catalysts are rather complex materials that come in many classes (e.g., metals, oxides, carbides) and shapes. At the same time, the interaction of the catalyst surface with even a relatively simple gas-phase environment such as syngas (CO and H₂) may already produce a wide variety of reaction intermediates ranging from atoms to complex molecules. The starting point for creating predictive maps of, e.g., surface coverages or chemical activities of potential catalyst materials is the reliable prediction of adsorption enthalpies of all of these intermediates. For simple systems, direct density functional theory (DFT) calculations are currently the method of choice. However, a wider exploration of complex materials and reaction networks generally requires enthalpy predictions at lower computational cost.The use of machine learning (ML) and related techniques to make accurate and low-cost predictions of quantum-mechanical calculations has gained increasing attention lately. The employed approaches span from physically motivated models over hybrid physics-ΔML approaches to complete black-box methods such as deep neural networks. In recent works we have explored the possibilities for using a compressed sensing method (Sure Independence Screening and Sparsifying Operator, SISSO) to identify sparse (low-dimensional) descriptors for the prediction of adsorption enthalpies at various active-site motifs of metals and oxides. We start from a set of physically motivated primary features such as atomic acid/base properties, coordination numbers, or band moments and let the data and the compressed sensing method find the best algebraic combination of these features. Here we take this work as a starting point to categorize and compare recent ML-based approaches with a particular focus on model sparsity, data efficiency, and the level of physical insight that one can obtain from the model.Looking ahead, while many works to date have focused only on the mere prediction of databases of, e.g., adsorption enthalpies, there is also an emerging interest in our field to start using ML predictions to answer fundamental science questions about the functioning of heterogeneous catalysts or perhaps even to design better catalysts than we know today. This task is significantly simplified in works that make use of scaling-relation-based models (volcano curves), where the model outcome is determined by only one or two adsorption enthalpies and which consequently become the sole target for ML-based high-throughput screening or design. However, the availability of cheap ML energetics also allows going beyond scaling relations. On the basis of our own work in this direction, we will discuss the additional physical insight that can be achieved by integrating ML-based predictions with traditional catalysis modeling techniques from thermal and electrocatalysis, such as the computational hydrogen electrode and microkinetic modeling, as well as the challenges that lie ahead.

Born to be different: the formation process of Cu nanoparticles tunes the size trend of the activity for CO2 to CH4 conversion

We investigate the impact of the formation process of Cu nanoparticles on the distribution of adsorption sites and hence on their activity. Using molecular dynamics, we model formation pathways characteristic of physical synthesis routes as the annealing of a liquid droplet, the growth proceeding via the addition of single atoms, and the coalescence of individual nanoparticles. Each formation process leads to different and characteristic size-dependent distributions of their adsorption sites, catalogued and monitored on-the-fly by means of a suitable geometrical descriptor. Annealed or coalesced nanoparticles present a rather homogeneous distribution in the kind and relative abundance of non-equivalent adsorption sites. Atom-by-atom grown nanoparticles, instead, exhibit a more marked occurrence of adsorption sites corresponding to adatoms and small islands on (111) and (100) facets. Regardless of the formation process, highly coordinated sites are more likely in larger nanoparticles, while the abundance of low-coordination sites depends on the formation process and on the nanoparticle size. Furthermore, we show how each characteristic distribution of adsorption sites reflects in different size trends for the Cu-nanoparticle activity, taking as an example the electro-reduction of CO₂ into CH₄. To this end, we employ a multi-scale method and observe that the faceted but highly defected structures obtained during the atom-by-atom growth become more and more active with increasing size, with a mild dependence on the original seed. In contrast, the activity of Cu-nanoparticles obtained by annealing decreases with their size, while coalesced nanoparticles' activity shows a non-monotonic behaviour.

The sequential activation of H2 and N2 mediated by the gas-phase Sc3N+ clusters: Formation of amido unit

The activation and hydrogenation of nitrogen are central in industry and in nature. Through a combination of mass spectrometry and quantum chemical calculations, this work reports an interesting result that scandium nitride cations Sc₃N⁺ can activate sequentially H₂ and N₂, and an amido unit (NH₂) is formed based on density functional theory calculations, which is one of the inevitable intermediates in the N₂ reduction reactions. If the activation step is reversed, i.e., sequential activation of first N₂ and then H₂, the reactivity decreases dramatically. An association mechanism, prevalent in some homogeneous catalysis and enzymatic mechanisms, is adopted in these gas-phase H₂ and N₂ activation reactions mediated by Sc₃N⁺ cations. The mechanistic insights are important to understand the mechanism of the conversion of H₂ and N₂ to NH₃ synthesis under ambient conditions.

Theoretical insights into single-atom catalysts

Schematic diagram of theoretical models and applications of single atom catalysts. A review on the theoretical models, intrinsic properties, and the related application of SACs.

Systematic Enumeration of Elementary Reaction Steps in Surface Catalysis

The direct synthesis of complex chemicals from simple precursors (such as syngas) is one of the main objectives of current research in heterogeneous catalysis. To rationally design catalytic materials for this purpose, it is essential to identify the critical elementary reaction steps that ultimately determine a catalyst's activity and selectivity with respect to a desired product. Unfortunately, the number of potentially relevant elementary steps is in the thousands, even for relatively simple target species like ethanol. The challenge of identifying the critical steps is thus akin to finding the proverbial needle in a haystack. Recently, a model-reduction scheme has been proposed, which tackles this problem by prescreening the barriers of all potential reactions with computationally inexpensive approximations. Although this route appears highly promising, it raises the question of how the starting point of the model-reduction process can be determined. In this contribution, we present a systematic method for enumerating all intermediates and elementary reactions relevant to a chemical process of interest. Using this approach, we construct reaction networks for C,H,O-containing systems consisting of up to four non-hydrogen atoms (more than 1 million reactions). Importantly, the scheme goes beyond simple bond-breaking reactions and allows considering rearrangement and transfer reactions as well. The presented reaction networks thus cover the chemistry of syngas-based processes (and beyond) to an unprecedented scale.

General scaling relations and prediction of transition state energies in CHA/AlPO-34-structured zeolite catalysis related to the methanol-to-olefins conversion

Scaling relations of transition state (TS) energies with the acid strength were established. The inherent scaling relations and the acidity sensitivity dependence on charge variation enable fast prediction of TS energies in zeolite catalysis.

Towards operando computational modeling in heterogeneous catalysis

An increased synergy between experimental and theoretical investigations in heterogeneous catalysis has become apparent during the last decade. Experimental work has extended from ultra-high vacuum and low temperature towards operando conditions. These developments have motivated the computational community to move from standard descriptive computational models, based on inspection of the potential energy surface at 0 K and low reactant concentrations (0 K/UHV model), to more realistic conditions. The transition from 0 K/UHV to operando models has been backed by significant developments in computer hardware and software over the past few decades. New methodological developments, designed to overcome part of the gap between 0 K/UHV and operando conditions, include (i) global optimization techniques, (ii) ab initio constrained thermodynamics, (iii) biased molecular dynamics, (iv) microkinetic models of reaction networks and (v) machine learning approaches. The importance of the transition is highlighted by discussing how the molecular level picture of catalytic sites and the associated reaction mechanisms changes when the chemical environment, pressure and temperature effects are correctly accounted for in molecular simulations. It is the purpose of this review to discuss each method on an equal footing, and to draw connections between methods, particularly where they may be applied in combination.

Ho-Mediated Alkyne Reactions at Low Temperatures on Ag(111)

Low-temperature approaches to catalytic conversions promise efficiency, selectivity, and sustainable processes. Control over certain coupling reactions can be obtained via the pre-positioning of reactive moieties by self-assembly. However, in the striving field of on-surface synthesis atomistic precision and control remains largely elusive, because the employed coupling reactions proceed at temperatures beyond the thermal stability of the supramolecular templates. Here, utilizing scanning tunneling microscopy, we demonstrate terminal alkyne on-surface reactions mediated by Ho atoms at a weakly reactive Ag(111) substrate at low-temperatures. Density functional theory calculations confirm the catalytic activity of the involved adatoms. Pre-deposited Ho induces alkyne dehydrogenation starting at substrate temperatures as low as 100 K. Ho arriving at molecularly pre-covered surfaces held at 130 and 200 K produces covalent enyne-linked dimers and initiates cyclotrimerization, respectively. Statistical product analysis indicates a two-step pathway for the latter, whereby the enyne intermediates influence the distribution of the products. High chemoselectivity results from the absence of cyclotetramerization and diyne-forming homocoupling. Our analysis indicates that mainly the arriving Ho adatoms enable the coupling. These findings support the concept of dynamic heterogeneity by single-atom catalysts and pave the way for alternative means to control on-surface reactions.

Assessment of mean-field microkinetic models for CO methanation on stepped metal surfaces using accelerated kinetic Monte Carlo

First-principles screening studies aimed at predicting the catalytic activity of transition metal (TM) catalysts have traditionally been based on mean-field (MF) microkinetic models, which neglect the effect of spatial correlations in the adsorbate layer. Here we critically assess the accuracy of such models for the specific case of CO methanation over stepped metals by comparing to spatially resolved kinetic Monte Carlo (kMC) simulations. We find that the typical low diffusion barriers offered by metal surfaces can be significantly increased at step sites, which results in persisting correlations in the adsorbate layer. As a consequence, MF models may overestimate the catalytic activity of TM catalysts by several orders of magnitude. The potential higher accuracy of kMC models comes at a higher computational cost, which can be especially challenging for surface reactions on metals due to a large disparity in the time scales of different processes. In order to overcome this issue, we implement and test a recently developed algorithm for achieving temporal acceleration of kMC simulations. While the algorithm overall performs quite well, we identify some challenging cases which may lead to a breakdown of acceleration algorithms and discuss possible directions for future algorithm development.

Heterogeneous Fe3 single-cluster catalyst for ammonia synthesis via an associative mechanism

The current industrial ammonia synthesis relies on Haber–Bosch process that is initiated by the dissociative mechanism, in which the adsorbed N₂ dissociates directly, and thus is limited by Brønsted–Evans–Polanyi (BEP) relation. Here we propose a new strategy that an anchored Fe₃ cluster on the θ-Al₂O₃(010) surface as a heterogeneous catalyst for ammonia synthesis from first-principles theoretical study and microkinetic analysis. We have studied the whole catalytic mechanism for conversion of N₂ to NH₃ on Fe₃/θ-Al₂O₃(010), and find that an associative mechanism, in which the adsorbed N₂ is first hydrogenated to NNH, dominates over the dissociative mechanism, which we attribute to the large spin polarization, low oxidation state of iron, and multi-step redox capability of Fe₃ cluster. The associative mechanism liberates the turnover frequency (TOF) for ammonia production from the limitation due to the BEP relation, and the calculated TOF on Fe₃/θ-Al₂O₃(010) is comparable to Ru B5 site.

The current industrial ammonia synthesis relies on the Haber-Bosch process that is limited by the Brønsted–Evans–Polanyi relation. Here, the authors propose a new strategy that an anchored Fe₃ on θ-Al₂O₃(010) surface serves as a heterogeneous single cluster catalyst for ammonia synthesis from first-principles calculations and microkinetic analysis.

Scaling Relations for Acidity and Reactivity of Zeolites

Zeolites are widely applied as solid acid catalysts in various technological processes. In this work we have computationally investigated how catalytic reactivity scales with acidity for a range of zeolites with different topologies and chemical compositions. We found that straightforward correlations are limited to zeolites with the same topology. The adsorption energies of bases such as carbon monoxide (CO), acetonitrile (CH3CN), ammonia (NH3), trimethylamine (N(CH3)3), and pyridine (C5H5N) give the same trend of acid strength for FAU zeolites with varying composition. Crystal orbital Hamilton populations (COHP) analysis provides a detailed molecular orbital picture of adsorbed base molecules on the Brønsted acid sites (BAS). Bonding is dominated by strong σ donation from guest molecules to the BAS for the adsorbed CO and CH3CN complexes. An electronic descriptor of acid strength is constructed based on the bond order calculations, which is an intrinsic parameter rather than adsorption energy that contains additional contributions due to secondary effects such as van der Waals interactions with the zeolite walls. The bond order parameter derived for the CH3CN adsorption complex represents a useful descriptor for the intrinsic acid strength of FAU zeolites. For FAU zeolites the activation energy for the conversion of π-adsorbed isobutene into alkoxy species correlates well with the acid strength determined by the NH3 adsorption energies. Other zeolites such as MFI and CHA do not follow the scaling relations obtained for FAU; we ascribe this to the different van der Waals interactions and steric effects induced by zeolite framework topology.

Grand challenges for catalysis in the Science and Technology Roadmap on Catalysis for Europe: moving ahead for a sustainable future

This perspective discusses the general concepts that will guide future catalysis and related grand challenges based on the Science and Technology Roadmap on Catalysis for Europe prepared by the European Cluster on Catalysis.

Hydrogen adsorption on MoS2-surfaces: a DFT study on preferential sites and the effect of sulfur and hydrogen coverage

H-Adsorption on MoS₂-surfaces is studied as a function of structural parameters and an assessment of the intricate structure–property relations is conducted.