Selectivity of Synthesis Gas Conversion to C2+ Oxygenates on fcc(111) Transition-Metal Surfaces

In Situ Surfaced Mn-Mn Dimeric Sites Dictate CO Hydrogenation Activity and C2 Selectivity over MnRh Binary Catalysts

Massive ethanol production has long been a dream of human society. Despite extensive research in past decades, only a few systems have the potential of industrialization: specifically, Mn-promoted Rh (MnRh) binary heterogeneous catalysts were shown to achieve up to 60% C2 oxygenates selectivity in converting syngas (CO/H2) to ethanol. However, the active site of the binary system has remained poorly characterized. Here, large-scale machine-learning global optimization is utilized to identify the most stable Mn phases on Rh metal surfaces under reaction conditions by exploring millions of likely structures. We demonstrate that Mn prefers the subsurface sites of Rh metal surfaces and is able to emerge onto the surface forming MnRh surface alloy once the oxidative O/OH adsorbates are present. Our machine-learning-based transition state exploration further helps to resolve automatedly the whole reaction network, including 74 elementary reactions on various MnRh surface sites, and reveals that the Mn-Mn dimeric site at the monatomic step edge is the true active site for C2 oxygenate formation. The turnover frequency of the C2 product on the Mn-Mn dimeric site at MnRh steps is at least 107 higher than that on pure Rh steps from our microkinetic simulations, with the selectivity to the C2 product being 52% at 523 K. Our results demonstrate the key catalytic role of Mn-Mn dimeric sites in allowing C-O bond cleavage and facilitating the hydrogenation of O-terminating C2 intermediates, and rule out Rh metal by itself as the active site for CO hydrogenation to C2 oxygenates.

Implementing the Blowers-Masel Approximation to Scale Activation Energy Based on Reaction Enthalpy in Mean-Field Microkinetic Modeling for Catalytic Methane Partial Oxidation

Mean-field microkinetic modeling is a powerful tool for catalyst design and the simulation of catalytic processes. The reaction enthalpies in a microkinetic model often need to be adjusted when changing species' binding energies to model different catalysts, when performing thermodynamic sensitivity analyses, and when fitting experimental data. When altering reaction enthalpies, the activation energies should also be reasonably altered to ensure realistic reaction rates. The Blowers-Masel approximation (BMA) relates the reaction barrier to the reaction enthalpy. Unlike the Brønsted-Evans-Polani relationship, the BMA requires less data because only one parameter, the intrinsic activation energy, needs to be determined. We validate this application of BMA relations to model surface reactions by comparing against density functional theory data taken from the literature. By incorporating the BMA rate description into the open-source Cantera software, we enable a new workflow, demonstrated herein, allowing rapid screening of catalysts using linear scaling relationships and BMA kinetics within the process simulation software. For demonstration purposes, a catalyst screening for catalytic methane partial oxidation on 81 hypothetical metals is conducted. We compared the results with and without BMA-corrected rates. The heat maps of various descriptors (e.g., CH4 conversion, syngas yield) show that using BMA rates instead of Arrhenius rates (with constant activation energies) changes which metals are most active. Heat maps of sensitivity analyses can help identify which reactions or species are the most influential in shaping the descriptor map patterns. Our findings indicate that while using BMA-adjusted rates did not markedly affect the most sensitive reactions, it did change the most influential species.

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.

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.

Pynta─An Automated Workflow for Calculation of Surface and Gas-Surface Kinetics

Many important industrial processes rely on heterogeneous catalytic systems. However, given all possible catalysts and conditions of interest, it is impractical to optimize most systems experimentally. Automatically generated microkinetic models can be used to efficiently consider many catalysts and conditions. However, these microkinetic models require accurate estimation of many thermochemical and kinetic parameters. Manually calculating these parameters is tedious and error prone, involving many interconnected computations. We present Pynta, a workflow software for automating the calculation of surface and gas-surface reactions. Pynta takes the reactants, products, and atom maps for the reactions of interest, generates sets of initial guesses for all species and saddle points, runs all optimizations, frequency, and IRC calculations, and computes the associated thermochemistry and rate coefficients. It is able to consider all unique adsorption configurations for both adsorbates and saddle points, allowing it to handle high index surfaces and bidentate species. Pynta implements a new saddle point guess generation method called harmonically forced saddle point searching (HFSP). HFSP defines harmonic potentials based on the optimized adsorbate geometries and which bonds are breaking and forming that allow initial placements to be optimized using the GFN1-xTB semiempirical method to create reliable saddle point guesses. This method is reaction class agnostic and fast, allowing Pynta to consider all possible adsorbate site placements efficiently. We demonstrate Pynta on 11 diverse reactions involving monodenate, bidentate, and gas-phase species, many distinct reaction classes, and both a low and a high index facet of Cu. Our results suggest that it is very important to consider reactions between adsorbates adsorbed in all unique configurations for interadsorbate group transfers and reactions on high index surfaces.

Machine Learning and Quantum Calculation for Predicting Yield in Cu-Catalyzed P-H Reactions

The paper discussed the use of machine learning (ML) and quantum chemistry calculations to predict the transition state and yield of copper-catalyzed P-H insertion reactions. By analyzing a dataset of 120 experimental data points, the transition state was determined using density functional theory (DFT). ML algorithms were then applied to analyze 16 descriptors derived from the quantum chemical transition state to predict the product yield. Among the algorithms studied, the Support Vector Machine (SVM) achieved the highest prediction accuracy of 97%, with over 80% correlation in Leave-One-Out Cross-Validation (LOOCV). Sensitivity analysis was performed on each descriptor, and a comprehensive investigation of the reaction mechanism was conducted to better understand the transition state characteristics. Finally, the ML model was used to predict reaction plans for experimental design, demonstrating strong predictive performance in subsequent experimental validation.

Identifying Descriptors for Promoted Rhodium-Based Catalysts for Higher Alcohol Synthesis via Machine Learning

Rhodium-based catalysts offer remarkable selectivities toward higher alcohols, specifically ethanol, via syngas conversion. However, the addition of metal promoters is required to increase reactivity, augmenting the complexity of the system. Herein, we present an interpretable machine learning (ML) approach to predict and rationalize the performance of Rh-Mn-P/SiO2 catalysts (P = 19 promoters) using the open-source dataset on Rh-catalyzed higher alcohol synthesis (HAS) from Pacific Northwest National Laboratory (PNNL). A random forest model trained on this dataset comprising 19 alkali, transition, post-transition metals, and metalloid promoters, using catalytic descriptors and reaction conditions, predicts the higher alcohols space-time yield (STYHA) with an accuracy of R 2 = 0.76. The promoter's cohesive energy and alloy formation energy with Rh are revealed as significant descriptors during posterior feature-importance analysis. Their interplay is captured as a dimensionless property, coined promoter affinity index (PAI), which exhibits volcano correlations for space-time yield. Based on this descriptor, we develop guidelines for the rational selection of promoters in designing improved Rh-Mn-P/SiO2 catalysts. This study highlights ML as a tool for computational screening and performance prediction of unseen catalysts and simultaneously draws insights into the property-performance relations of complex catalytic systems.

Screening of bimetallic electrocatalysts for water purification with machine learning

Electrocatalysis provides a potential solution to [Formula: see text] pollution in wastewater by converting it to innocuous N2 gas. However, materials with excellent catalytic activity are typically limited to expensive precious metals, hindering their commercial viability. In response to this challenge, we have conducted the most extensive computational search to date for electrocatalysts that can facilitate [Formula: see text] reduction reaction, starting with 59 390 candidate bimetallic alloys from the Materials Project and Automatic-Flow databases. Using a joint machine learning- and computation-based screening strategy, we evaluated our candidates based on corrosion resistance, catalytic activity, N2 selectivity, cost, and the ability to synthesize. We found that only 20 materials will satisfy all criteria in our screening strategy, all of which contain varying amounts of Cu. Our proposed list of candidates is consistent with previous materials investigated in the literature, with the exception of Cu–Co and Cu–Ag based compounds that merit further investigation.

Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation

Computational screening in heterogeneous catalysis relies increasingly on machine learning models for predicting key input parameters due to the high cost of computing these directly using first-principles methods. This becomes especially relevant when considering complex materials spaces such as alloys, or complex reaction mechanisms with adsorbates that may exhibit bi- or higher-dentate adsorption motifs. Here we present a data-efficient approach to the prediction of binding motifs and associated adsorption enthalpies of complex adsorbates at transition metals and their alloys based on a customized Wasserstein Weisfeiler-Lehman graph kernel and Gaussian process regression. The model shows good predictive performance, not only for the elemental transition metals on which it was trained, but also for an alloy based on these transition metals. Furthermore, incorporation of minimal new training data allows for predicting an out-of-domain transition metal. We believe the model may be useful in active learning approaches, for which we present an ensemble uncertainty estimation approach.

Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights

The electrochemical nitrate reduction reaction (NO₃RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metal d-states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO₃ is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO₃RR to ammonia with a Faradaic efficiency of 92.5% at -0.5 V_RHE and a yield rate of 6.25 mol h^-1 g^-1 at -0.6 V_RHE. This study provides machine-learned design rules besides the d-band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations.

Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility

Mechanistic understanding of large molecule conversion and the discovery of suitable heterogeneous catalysts have been lagging due to the combinatorial inventory of intermediates and the inability of humans to enumerate all structures. Here, we introduce an automated framework to predict stable configurations on transition metal surfaces and demonstrate its validity for adsorbates with up to 6 carbon and oxygen atoms on 11 metals, enabling the exploration of ~10⁸ potential configurations. It combines a graph enumeration platform, force field, multi-fidelity DFT calculations, and first-principles trained machine learning. Clusters in the data reveal groups of catalysts stabilizing different structures and expose selective catalysts for showcase transformations, such as the ethylene epoxidation on Ag and Cu and the lack of C-C scission chemistry on Au. Deviations from the commonly assumed atom valency rule of small adsorbates are also manifested. This library can be leveraged to identify catalysts for converting large molecules computationally.

The discovery of heterogeneous catalysts for large molecule conversion has been lagging due to the combinatorial inventory of intermediates. Here, the author presents an automated framework to explore the chemical space of reaction intermediates.

Operando Observation of Oxygenated Intermediates during CO Hydrogenation on Rh Single Crystals

The CO hydrogenation reaction over the Rh(111) and (211) surfaces has been investigated operando by X-ray photoelectron spectroscopy at a pressure of 150 mbar. Observations of the resting state of the catalyst give mechanistic insight into the selectivity of Rh for generating ethanol from CO hydrogenation. This study shows that the Rh(111) surface does not dissociate all CO molecules before hydrogenation of the O and C atoms, which allows methoxy and other both oxygenated and hydrogenated species to be visible in the photoelectron spectra.

Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion

Catlas may be used with off-the-shelf pretrained models to explore large design spaces for catalyst discovery and has been used here to identify promising materials for the direct conversion of syngas to multi-carbon oxygenates.

Diffusion Barriers for Carbon Monoxide on the Cu(001) Surface Using Many-Body Perturbation Theory and Various Density Functionals

First-principles calculations play a key role in understanding the interactions of molecules with transition-metal surfaces and the energy profiles for catalytic reactions. However, many of the commonly used density functionals are not able to correctly predict the surface energy as well as the adsorption site preference for a key molecule such as CO, and it is not clear to what extent this shortcoming influences the prediction of reaction or diffusion pathways. Here, we report calculations of carbon monoxide diffusion on the Cu(001) surface along the [100] and [110] pathways, as well as the surface energy of Cu(001), and CO-adsorption energy and compare the performance of the Perdew-Burke-Ernzerhof (PBE), PBE + D2, PBE + D3, RPBE, Bayesian error estimation functional with van der Waals correlation (BEEF-vdW), HSE06 density functionals, and the random phase approximation (RPA), a post-Hartree-Fock method based on many-body perturbation theory. We critically evaluate the performance of these methods and find that RPA appears to be the only method giving correct site preference, overall barrier, adsorption enthalpy, and surface energy. For all of the other methods, at least one of these properties is not correctly captured. These results imply that many density functional theory (DFT)-based methods lead to qualitative and quantitative errors in describing CO interaction with transition-metal surfaces, which significantly impacts the description of diffusion pathways. It is well conceivable that similar effects exist when surface reactions of CO-related species are considered. We expect that the methodology presented here will be used to get more detailed insights into reaction pathways for CO conversion on transition-metal surfaces in general and Cu in particular, which will allow us to better understand the catalytic and electrocatalytic reactions involving CO-related species.

Moderate Surface Segregation Promotes Selective Ethanol Production in CO2 Hydrogenation Reaction over CoCu Catalysts

Cobalt-copper (CoCu) catalysts have industrial potential in CO/CO₂ hydrogenation reactions, and CoCu alloy has been elucidated as a major active phase during reactions. However, due to elemental surface segregation and dealloying phenomena, the actual surface morphology of CoCu alloy is still unclear. Combining theory and experiment, the dual effect of surface segregation and varied CO coverage over the CoCu(111) surface on the reactivity in CO₂ hydrogenation reactions is explored. The relationship between C-O bond scission and further hydrogenation of intermediate *CH₂ O was discovered to be a key step to promote ethanol production. The theoretical investigation suggests that moderate Co segregation provides a suitable surface Co ensemble with lateral interactions of co-adsorbed *CO, leading to promoted selectivity to ethanol, in agreement with theory-inspired experiments.

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for example, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3-55 atoms with mean absolute errors in the range of 0.11-0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure-property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal-metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3-55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.

Research progress of catalysts for synthesis of low-carbon alcohols from synthesis gas

In recent years, the application value of low-carbon alcohols (C1–C6 alcohols) in the fuel, chemical, environmental protection and other fields has become increasingly prominent. Catalytic conversion of synthesis gas to low-carbon alcohol is one of the important ways to realize the industrial production of low-carbon alcohol. Lack of high-performance catalysts is the main reason that restricts the industrial development of producing low-carbon alcohols from synthesis gas. The construction of a dual active-center catalyst with high activity and stability, and the study of its function and catalytic mechanism have become significantly important. In this paper, the characteristics of the reaction process of syngas to low-carbon alcohols, and the catalytic mechanism and preparation methods of different catalyst systems were reviewed, which provide the basis for further research on high performance catalysts.

The reaction process of the CO hydrogenation catalyzed synthesis of lower alcohols.

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.

First-principles microkinetic simulations revealing the scaling relations and structure sensitivity of CO2 hydrogenation to C1 & C2 oxygenates on Pd surfaces

CO₂ hydrogenation to alcohols and other oxygenates on Pd(211) and Pd(111) surfaces was studied by microkinetic modelling. Energy scaling relations on two surfaces were established. Activity plots as a function of reaction conditions were identified.

Combining Theory and Experiment to Characterize the Voltammetric Behavior of Nickel Anodes in the Simons Process

The Simons process, otherwise known as the electrochemical fluorination (ECF) method, is widely used in industry to electrolytically synthesize chemicals for various purposes. Even to this day, the exact mechanism of the ECF reaction remains unknown, but is believed to involve the formation of an anodic nickel fluoride film with highly oxidized nickel centers. In this study, experiments and density functional theory calculations are combined to characterize the initial anodic peak occurring at potentials typically required in an ECF cell. NiF2 is believed to form a passivating layer at low potentials. The calculations show that a potential of +3.1 V is required to oxidize surface Ni2+ centers to Ni3+ . This is in good agreement with the measured anodic peak at +3.57 V.

Predicting metal-metal interactions. II. Accelerating generalized schemes through physical insights

Operando-computational frameworks that integrate descriptors for catalyst stability within catalyst screening paradigms enable predictions of rates and selectivity on chemically faithful representations of nanoparticles under reaction conditions. These catalyst stability descriptors can be efficiently predicted by density functional theory (DFT)-based models. The alloy stability model, for example, predicts the stability of metal atoms in nanoparticles with site-by-site resolution. Herein, we use physical insights to present accelerated approaches of parameterizing this recently introduced alloy-stability model. These accelerated approaches meld quadratic functions for the energy of metal atoms in terms of the coordination number with linear correlations between model parameters and the cohesive energies of bulk metals. By interpolating across both the coordination number and chemical space, these accelerated approaches shrink the training set size for 12 fcc p- and d-block metals from 204 to as few as 24 DFT calculated total energies without sacrificing the accuracy of our model. We validate the accelerated approaches by predicting adsorption energies of metal atoms on extended surfaces and 147 atom cuboctahedral nanoparticles with mean absolute errors of 0.10 eV and 0.24 eV, respectively. This efficiency boost will enable a rapid and exhaustive exploration of the vast material space of transition metal alloys for catalytic applications.

Predicting metal-metal interactions. I. The influence of strain on nanoparticle and metal adlayer stabilities

Strain-engineering of bimetallic nanomaterials is an important design strategy for developing new catalysts. Herein, we introduce an approach for including strain effects into a recently introduced, density functional theory (DFT)-based alloy stability model. The model predicts adsorption site stabilities in nanoparticles and connects these site stabilities with catalytic reactivity and selectivity. Strain-based dependencies will increase the model's accuracy for nanoparticles affected by finite-size effects. In addition to the stability of small nanoparticles, strain also influences the heat of adsorption of epitaxially grown metal-on-metal adlayers. In this respect, we successfully benchmark the strain-including alloy stability model with previous experimentally determined trends in the heats of adsorption of Au and Cu adlayers on Pt (111). For these systems, our model predicts stronger bimetallic interactions in the first monolayer than monometallic interactions in the second monolayer. We explicitly quantify the interplay between destabilizing strain effects and the energy gained by forming new metal-metal bonds. While tensile strain in the first Cu monolayer significantly destabilizes the adsorption strength, compressive strain in the first Au monolayer has a minimal impact on the heat of adsorption. Hence, this study introduces and, by comparison with previous experiments, validates an efficient DFT-based approach for strain-engineering the stability, and, in turn, the catalytic performance, of active sites in bimetallic alloys with atomic level resolution.

Mechanistic insights into higher alcohol synthesis from syngas on Rh/Cu single-atom alloy catalysts

DFT calculations show that C–C coupling is more favored on Rh/Cu single-atom alloy catalysts than on pure metal catalysts.

Understanding Structure-Property Relationships of MoO3-Promoted Rh Catalysts for Syngas Conversion to Alcohols

Rh-based catalysts have shown promise for the direct conversion of syngas to higher oxygenates. Although improvements in higher oxygenate yield have been achieved by combining Rh with metal oxide promoters, details of the structure of the promoted catalyst and the role of the promoter in enhancing catalytic performance are not well understood. In this work, we show that MoO₃-promoted Rh nanoparticles form a novel catalyst structure in which Mo substitutes into the Rh surface, leading to both a 66-fold increase in turnover frequency and an enhancement in oxygenate yield. By applying a combination of atomically controlled synthesis, in situ characterization, and theoretical calculations, we gain an understanding of the promoter-Rh interactions that govern catalytic performance for MoO₃-promoted Rh. We use atomic layer deposition to modify Rh nanoparticles with monolayer-precise amounts of MoO₃, with a high degree of control over the structure of the catalyst. Through in situ X-ray absorption spectroscopy, we find that the atomic structure of the catalytic surface under reaction conditions consists of Mo-OH species substituted into the surface of the Rh nanoparticles. Using density functional theory calculations, we identify two roles of MoO₃: first, the presence of Mo-OH in the catalyst surface enhances CO dissociation and also stabilizes a methanol synthesis pathway not present in the unpromoted catalyst; and second, hydrogen spillover from Mo-OH sites to adsorbed species on the Rh surface enhances hydrogenation rates of reaction intermediates.

Development and Assessment of a Criterion for the Application of Brønsted-Evans-Polanyi Relations for Dissociation Catalytic Reactions at Surfaces

We propose and assess a criterion for the application of Brønsted–Evans–Polanyi (BEP) relations for dissociation reactions at surfaces. A theory-to-theory comparison with density functional theory calculations is presented on different reactions, metal catalysts, and surface terminations. In particular, the activation energies of CH, CO, and trans-COOH dissociation reactions on (100), (110), (111), and (211) surfaces of Ni, Cu, Rh, Pd, Ag, and Pt are considered. We show that both the activation energy and the reaction energy can be decomposed into two contributions that reflect the influence of reactant and products in determining either the activation energy or the reaction energy. We show that the applicability of the BEP relation implies that the reaction energy and activation energy correlate to these two contributions in the range of conditions to be described by the BEP relation. A lack of correlation between these components for the activation energy is related to a change in the character of the transition state (TS) and this turns out to be incompatible with a BEP relation because it results in a change of the slope of the BEP relation. Our analysis reveals that these two contributions follow the same trends for the activation energy and for the reaction energy when the path is not characterized either by the formation of stable intermediates or by the change of the binding mechanism of the reactant. As such, one can assess whether a BEP relation can be applied or not for a set of conditions only by means of thermochemical calculations and without requiring the identification of the TS along the reaction pathway. We provide evidence that this criterion can be successfully applied for the preliminary discrimination of the applicability of the BEP relations. For instance, on the one hand, our analysis provides evidence that the two contributions are fully anticorrelated for the trans-COOH dissociation reactions on different metals and surfaces, thus revealing that the reaction is characterized by a change in the TS character. In this situation, no BEP relation can be used to describe the activation energy trend among the different metals and surfaces in full agreement with our DFT calculations. On the other hand, our criterion reveals that the TS character is not expected to change for CH dissociation reactions both for the same facet, different metals and for same metal, different facets, in good agreement with the DFT calculations of the activation energy. The formation of multiple stable intermediates along the reaction pathways and the change of the binding mechanism of one of the reactants are demonstrated to affect the validity of the criterion. As a whole, our findings make possible an assessment of the applicability of the BEP relation and paves the way toward its use for the exploration of complex reaction networks for different metals and surfaces.

Catalysis-Hub.org, an open electronic structure database for surface reactions

We present a new open repository for chemical reactions on catalytic surfaces, available at https://www.catalysis-hub.org . The featured database for surface reactions contains more than 100,000 chemisorption and reaction energies obtained from electronic structure calculations, and is continuously being updated with new datasets. In addition to providing quantum-mechanical results for a broad range of reactions and surfaces from different publications, the database features a systematic, large-scale study of chemical adsorption and hydrogenation on bimetallic alloy surfaces. The database contains reaction specific information, such as the surface composition and reaction energy for each reaction, as well as the surface geometries and calculational parameters, essential for data reproducibility. By providing direct access via the web-interface as well as a Python API, we seek to accelerate the discovery of catalytic materials for sustainable energy applications by enabling researchers to efficiently use the data as a basis for new calculations and model generation.

High-throughput calculations of catalytic properties of bimetallic alloy surfaces

A comprehensive database of chemical properties on a vast set of transition metal surfaces has the potential to accelerate the discovery of novel catalytic materials for energy and industrial applications. In this data descriptor, we present such an extensive study of chemisorption properties of important adsorbates - e.g., C, O, N, H, S, CHx, OH, NH, and SH - on 2,035 bimetallic alloy surfaces in 5 different stoichiometric ratios, i.e., 0%, 25%, 50%, 75%, and 100%. To our knowledge, it is the first systematic study to compile the adsorption properties of such a well-defined, large chemical space of catalytic interest. We propose that a collection of catalytic properties of this magnitude can assist with the development of machine learning enabled surrogate models in theoretical catalysis research to design robust catalysts with high activity for challenging chemical transformations. This database is made publicly available through the platform www.Catalysis-hub.org for easy retrieval of the data for further scientific analysis.

Predicting Adsorption Properties of Catalytic Descriptors on Bimetallic Nanoalloys with Site-Specific Precision

Bimetallic nanoparticles present a vastly tunable structural and compositional design space rendering them promising materials for catalytic and energy applications. Yet it remains an enduring challenge to efficiently screen candidate alloys with atomic level specificity while explicitly accounting for their inherent stabilities under reaction conditions. Herein, by leveraging correlations between binding energies of metal adsorption sites and metal-adsorbate complexes, we predict adsorption energies of typical catalytic descriptors (OH*, CH₃*, CH*, and CO*) on bimetallic alloys with site-specific resolution. We demonstrate that our approach predicts adsorption energies on top and bridge sites of bimetallic nanoparticles having generic morphologies and chemical environments with errors between 0.09 and 0.18 eV. By forging a link between the inherent stability of an alloy and the adsorption properties of catalytic descriptors, we can now identify active site motifs in nanoalloys that possess targeted catalytic descriptor values while being thermodynamically stable under working conditions.