Identifying Active Sites of the Water–Gas Shift Reaction over Titania Supported Platinum Catalysts under Uncertainty

Model-Based Design of Experiments for Temporal Analysis of Products (TAP): A Simulated Case Study in Oxidative Propane Dehydrogenation

Temporal analysis of products (TAP) reactors enable experiments that probe numerous kinetic processes within a single set of experimental data through variations in pulse intensity, delay, or temperature. Selecting additional TAP experiments often involves an arbitrary selection of reaction conditions or the use of chemical intuition. To make experiment selection in TAP more robust, we explore the efficacy of model-based design of experiments (MBDoE) for precision in TAP reactor kinetic modeling. We successfully applied this approach to a case study of synthetic oxidative propane dehydrogenation (OPDH) that involves pulses of propane and oxygen. We found that experiments identified as optimal through the MBDoE for precision generally reduce parameter uncertainties to a higher degree than alternative experiments. The performance of MBDoE for model divergence was also explored for OPDH, with the relevant active sites (catalyst structure) being unknown. An experiment that maximized the divergence between the three proposed mechanisms was identified and provided evidence that improved the mechanism discrimination. However, reoptimization of kinetic parameters eliminated the ability to discriminate between models. The findings yield insight into the prospects and limitations of MBDoE for TAP and transient kinetic experiments.

Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties

The study presents an ab-initio based framework for the automated construction of microkinetic mechanisms considering correlated uncertainties in all energetic parameters and estimation routines. 2000 unique microkinetic models were generated within the uncertainty space of the BEEF-vdW functional for the oxidation reactions of representative exhaust gas emissions from stoichiometric combustion engines over Pt(111) and compared to experiments through multiscale modeling. The ensemble of simulations stresses the importance of considering uncertainties. Within this set of first-principles-based models, it is possible to identify a microkinetic mechanism that agrees with experimental data. This mechanism can be traced back to a single exchange-correlation functional, and it suggests that Pt(111) could be the active site for the oxidation of light hydrocarbons. The study provides a universal framework for the automated construction of reaction mechanisms with correlated uncertainty quantification, enabling a DFT-constrained microkinetic model optimization for other heterogeneously catalyzed systems.

Differences in solvation thermodynamics of oxygenates at Pt/Al2O3 perimeter versus Pt(111) terrace sites

A prominent role of water in aqueous-phase heterogeneous catalysis is to modify free energies; however, intuition about how is based largely on pure metal surfaces or even homogeneous solutions. Using multiscale modeling with explicit liquid water molecules, we show that the influence of water on the free energies of adsorbates at metal/support interfaces is different than that on pure metal surfaces. We specifically compute free energies of solvation for methanol and its constituents on a Pt/Al₂O₃ catalyst and compare the results to analogous values calculated on a pure Pt catalyst. We find that the more hydrophilic Pt/Al₂O₃ interface leads to smaller (more positive) free energies of solvation due to an increased entropy penalty resulting from the additional work necessary to disrupt the interfacial water structure and accommodate the interfacial species. The results will be of interest in other fields, including adsorption and proteins.

A Review on the Different Aspects and Challenges of the Dry Reforming of Methane (DRM) Reaction

The dry reforming of methane (DRM) reaction is among the most popular catalytic reactions for the production of syngas (H₂/CO) with a H₂:CO ratio favorable for the Fischer-Tropsch reaction; this makes the DRM reaction important from an industrial perspective, as unlimited possibilities for production of valuable products are presented by the FT process. At the same time, simultaneously tackling two major contributors to the greenhouse effect (CH₄ and CO₂) is an additional contribution of the DRM reaction. The main players in the DRM arena-Ni-supported catalysts-suffer from both coking and sintering, while the activation of the two reactants (CO₂ and CH₄) through different approaches merits further exploration, opening new pathways for innovation. In this review, different families of materials are explored and discussed, ranging from metal-supported catalysts, to layered materials, to organic frameworks. DRM catalyst design criteria-such as support basicity and surface area, bimetallic active sites and promoters, and metal-support interaction-are all discussed. To evaluate the reactivity of the surface and understand the energetics of the process, density-functional theory calculations are used as a unique tool.

Hydrogen Production on Pt/TiO2: Synergistic Catalysis between Pt Clusters and Interfacial Adsorbates

Understanding the mechanism of hydrogen (H₂) formation from the conversion of water (H₂O) and renewables on TiO₂ surfaces with cocatalysts via either photocatalysis or other catalytic processes is of significant importance to the successful design of efficient catalysts. Herein, we have investigated H₂ production from H₂O, CH₃OH, and C₂H₅OH on a Pt cluster covered rutile (R)-TiO₂(110) surface (Pt_clut/R-TiO₂(110)) to address the mechanism of H₂ production. Experimental results demonstrate that surface adsorbates not only help H atom diffusion on Pt_clut/R-TiO₂(110) but also take part in H₂ production directly. Further density functional theory (DFT) calculations suggest that H₂ production on Pt_clut/R-TiO₂(110) occurs via a synergistic catalysis process between Pt clusters and interfacial adsorbates rather than a recombination reaction of H atoms on Pt clusters. This work provides new insight into H₂ production from H₂O and renewables with Pt/TiO₂ catalysts, which may be applicable to H₂ production on other Pt cluster deposited metal oxide catalysts.

Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles

Virtual high-throughput screening (VHTS) with density functional theory (DFT) and machine-learning (ML)-acceleration is essential in rapid materials discovery. By necessity, efficient DFT-based workflows are carried out with a single density functional approximation (DFA). Nevertheless, properties evaluated with different DFAs can be expected to disagree for cases with challenging electronic structure (e.g., open-shell transition-metal complexes, TMCs) for which rapid screening is most needed and accurate benchmarks are often unavailable. To quantify the effect of DFA bias, we introduce an approach to rapidly obtain property predictions from 23 representative DFAs spanning multiple families, “rungs” (e.g., semi-local to double hybrid) and basis sets on over 2000 TMCs. Although computed property values (e.g., spin state splitting and frontier orbital gap) differ by DFA, high linear correlations persist across all DFAs. We train independent ML models for each DFA and observe convergent trends in feature importance, providing DFA-invariant, universal design rules. We devise a strategy to train artificial neural network (ANN) models informed by all 23 DFAs and use them to predict properties (e.g., spin-splitting energy) of over 187k TMCs. By requiring consensus of the ANN-predicted DFA properties, we improve correspondence of computational lead compounds with literature-mined, experimental compounds over the typically employed single-DFA approach.

Machine learning (ML)-based feature analysis reveals universal design rules regardless of density functional choices. Using the consensus among multiple functionals, we identify robust lead complexes in ML-accelerated chemical discovery.

How a Quantum Computer Could Quantify Uncertainty in Microkinetic Models

A method of uncertainty quantification on a quantum circuit using three samples for the Rh(111)-catalyzed CO oxidation reaction is demonstrated. Three parametrized samples of a reduced, linearized microkinetic model populate a single block diagonal matrix for a quantum circuit. This approach leverages the logarithmic scaling of the number of qubits with respect to matrix size. The Harrow, Hassidim, and Lloyd (HHL) algorithm for solving linear systems is employed, and the results are compared with the classical results. This application area of uncertainty quantification in chemical kinetics can experience a quantum advantage using the method reported here, although issues related to larger systems are discussed.

Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis

Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.

How a Quantum Computer Could Solve a Microkinetic Model

A CO oxidation microkinetic model is set up for a quantum circuit. The CO oxidation microkinetic model, and microkinetic models in general, exhibit an advantage of not requiring an encoding step because of being a subclass of systems of equations. The microkinetic model is cast as a nonlinear set of equations at first. Then, a linearizing approximation is made, and the resulting linear set of equations may be iterated to converge to the solution to the nonlinear set of equations. In this CO oxidation, the linearized set of equations is realized to chemical accuracy with one iteration. Current limitations in executing the quantum circuit to obtain the solution are discussed.

Bayesian learning of chemisorption for bridging the complexity of electronic descriptors

Building upon the d-band reactivity theory in surface chemistry and catalysis, we develop a Bayesian learning approach to probing chemisorption processes at atomically tailored metal sites. With representative species, e.g., *O and *OH, Bayesian models trained with ab initio adsorption properties of transition metals predict site reactivity at a diverse range of intermetallics and near-surface alloys while naturally providing uncertainty quantification from posterior sampling. More importantly, this conceptual framework sheds light on the orbitalwise nature of chemical bonding at adsorption sites with d-states characteristics ranging from bulk-like semi-elliptic bands to free-atom-like discrete energy levels, bridging the complexity of electronic descriptors for the prediction of novel catalytic materials.

Developing a generalizable model to describe adsorption processes at metal surfaces can be extremely challenging due to complex phenomena involved. Here the authors introduce a Bayesian learning approach based on ab initio data and the d-band model to capture the essential physics of adsorbate–substrate interactions.

Progress in Computational and Machine-Learning Methods for Heterogeneous Small-Molecule Activation

The chemical conversion of small molecules such as H₂ , H₂ O, O₂ , N₂ , CO₂ , and CH₄ to energy and chemicals is critical for a sustainable energy future. However, the high chemical stability of these molecules poses grand challenges to the practical implementation of these processes. In this regard, computational approaches such as density functional theory, microkinetic modeling, data science, and machine learning have guided the rational design of catalysts by elucidating mechanistic insights, identifying active sites, and predicting catalytic activity. Here, the theory and methodologies for heterogeneous catalysis and their applications for small-molecule activation are reviewed. An overview of fundamental theory and key computational methods for designing catalysts, including the emerging data science techniques in particular, is given. Applications of these methods for finding efficient heterogeneous catalysts for the activation of the aforementioned small molecules are then surveyed. Finally, promising directions of the computational catalysis field for further outlooks are discussed, focusing on the challenges and opportunities for new methods.

Platinum Based Catalysts in the Water Gas Shift Reaction: Recent Advances

The water gas shift (WGS) is an equilibrium exothermic reaction, whose corresponding industrial process is normally carried out in two adiabatic stages, to overcome the thermodynamic and kinetic limitations. The high temperature stage makes use of iron/chromium-based catalysts, while the low temperature stage employs copper/zinc-based catalysts. Nevertheless, both these systems have several problems, mainly dealing with safety issues and process efficiency. Accordingly, in the last decade abundant researches have been focused on the study of alternative catalytic systems. The best performances have been obtained with noble metal-based catalysts, among which, platinum-based formulations showed a good compromise between performance and ease of preparation. These catalytic systems are extremely attractive, as they have numerous advantages, including the feasibility of intermediate temperature (250–400 °C) applications, the absence of pyrophoricity, and the high activity even at low loadings. The particle size plays a crucial role in determining their catalytic activity, enhancing the performance of the nanometric catalytic systems: the best activity and stability was reported for particle sizes < 1.7 nm. Moreover the optimal Pt loading seems to be located near 1 wt%, as well as the optimal Pt coverage was identified in 0.25 ML. Kinetics and mechanisms studies highlighted the low energy activation of Pt/Mo2C-based catalytic systems (Ea of 38 kJ·mol−1), the associative mechanism is the most encountered on the investigated studies. This review focuses on a selection of recent published articles, related to the preparation and use of unstructured platinum-based catalysts in water gas shift reaction, and is organized in five main sections: comparative studies, kinetics, reaction mechanisms, sour WGS and electrochemical promotion. Each section is divided in paragraphs, at the end of the section a summary and a summary table are provided.

Graph Theory Model of Dry Reforming of Methane Using Rh(111)

The adsorption energies of intermediates of the dry reforming of methane reaction (DRM CH₄ + CO₂ ⇔ 2CO + 2H₂) using Rh(111) are approximated. Graph theory creates descriptors of the intermediates. The information recorded in these descriptors includes the elemental identities of each atom, its neighbors, and its next-nearest neighbors. Graph theory is employed because it is a rapid approximation of more expensive density functional theory (DFT) calculations and because the descriptors created by graph theory are both human and machine interpretable. DRM contains a significant number of adsorbates, and side reactions, including reverse water-gas shift, may occur simultaneously. Therefore, DRM is well-poised for analysis by a graph theory model to predict large numbers of adsorption energies. A portion of adsorbates were calculated with DFT. Then, predictions were reported for the remaining adsorption energies not calculated with DFT.

Dynamic vs static behaviour of a supported nanoparticle with reaction-induced catalytic sites in a lattice model

Modern literature shows a rapidly growing interest to the supported nanocatalysts with dynamic behaviour under reaction conditions. This new frontier of heterogeneous catalysis is recognized as one of the most challenging and worthy of consideration from all possible angles. In this context, a previously suggested lattice model is used to get an insight, by means of kinetic Monte Carlo, into the influence of the mobility of reaction-induced catalytic sites of a two-dimensional supported nanoparticle on the system behaviour. The results speak in favour of feasibility of dynamic nanocatalysts with self-organized structures capable of robust functioning. This approach, from the macroscopic end, is believed to be a useful complement to ever developing experimental and first principle approaches.

Atomically dispersed platinum on low index and stepped ceria surfaces: phase diagrams and stability analysis

Through the combination of density functional theory calculations and ab initio atomistic thermodynamics modeling, we demonstrate that atomically dispersed platinum species on ceria can adopt a range of local coordination configurations and oxidation states that depend on the surface structure and environmental conditions. Unsaturated oxygen atoms on ceria surfaces play the leading role in stabilization of PtOx species. Any mono-dispersed Pt0 species are thermodynamically unstable compared to bulk platinum, and oxidation of Pt0 to Pt2+ or Pt4+ is necessary to stabilize mono-dispersed platinum atoms. Reduction to Pt0 leads to sintering. Both Pt2+ and Pt4+ prefer to form the square-planar [PtO4] configuration. The two most stable Pt2+ species on the (223) and (112) surfaces are thermodynamically favorable between 300 and 1200 K. The most stable Pt4+ species on the (100) surface tends to desorb from the surface as gas phase above 950 K. The resulting phase diagrams of the atomically dispersed platinum in PtOx clusters on various ceria surfaces under a range of experimentally relevant conditions can be used to predict dynamic restructuring of atomically dispersed platinum catalysts and design new catalysts with engineered properties.

Catalytic CO Oxidation by Noble-Metal-Free Ni2VO4,5- Clusters: A CO Self-Promoted Mechanism

Catalytic CO oxidation is an important model reaction in gas-phase studies to provide a clear structure-reactivity understanding in related heterogeneous catalysis, whereas CO oxidation catalyzed by noble-metal (NM) free species has been scarcely reported, and the fundamental aspects are elusive. Herein a CO self-promoted mechanism of catalytic CO oxidation by O₂ mediated with the Ni₂VO_4,5^- clusters was experimentally identified and theoretically rationalized. The catalysis was characterized by mass spectrometry and quantum chemistry calculations. Ni₂VO₅^- can oxidize CO to generate an oxygen-deficient product Ni₂VO₄^-, which can only adsorb CO to give rise to Ni₂VO₄CO^-, and the oxidative reactivity of Ni₂VO₄^- can be boosted by the adsorbed  CO. This finding reinforces the significance that the attached CO can modify the electronic structure of the Ni₂ unit in Ni₂VO₄CO^- and make the Ni₂ unit behave like NM atoms to store the released electrons in an oxygen atom transfer process.