Universal Brønsted-Evans-Polanyi Relations for C–C, C–O, C–N, N–O, N–N, and O–O Dissociation Reactions

Recent Advances in Revealing the Electrocatalytic Mechanism for Hydrogen Energy Conversion System

In light of the intensifying global energy crisis and the mounting demand for environmental protection, it is of vital importance to develop advanced hydrogen energy conversion systems. Electrolysis cells for hydrogen production and fuel cell devices for hydrogen utilization are indispensable in hydrogen energy conversion. As one of the electrolysis cells, water splitting involves two electrochemical reactions, hydrogen evolution reaction and oxygen evolution reaction. And oxygen reduction reaction coupled with hydrogen oxidation reaction, represent the core electrocatalytic reactions in fuel cell devices. However, the inherent complexity and the lack of a clear understanding of the structure-performance relationship of these electrocatalytic reactions, have posed significant challenges to the advancement of research in this field. In this work, the recent development in revealing the mechanism of electrocatalytic reactions in hydrogen energy conversion systems is reviewed, including in situ characterization and theoretical calculation. First, the working principles and applications of operando measurements in unveiling the reaction mechanism are systematically introduced. Then the application of theoretical calculations in the design of catalysts and the investigation of the reaction mechanism are discussed. Furthermore, the challenges and opportunities are also summarized and discussed for paving the development of hydrogen energy conversion systems.

Mechanistic insights into CO2 hydrogenation to methanol on Cu(110): unveiling energy linear relationships and enhancing performance strategies

The study of energy correlations in catalytic reactions plays a pivotal role in guiding catalyst development. This paper focuses on the investigation of energy linear relationships in methanol synthesis from CO2 hydrogenation on copper surfaces, systematically exploring energy parameters including activation energy, reaction energy and adsorption energy. A comparative analysis of the adsorption characteristics and reaction parameters in the formate, formic acid and reverse water-gas shift pathways is conducted, laying the data foundation for subsequent linear studies. Then, descriptors are extracted from electronic, energetic and structural information and further integrated using the sure independence screening and sparsifying operator (SISSO) method to establish an energy description paradigm characterized by interpretability and accuracy. Additionally, reactions are further categorized based on hydrogenation types to mitigate the adverse effects of redundant data points. Finally, the summarized reaction descriptors are extended to Cu-based alloy systems to highlight the rationality and transferability of the developed descriptors.

Toward Realistic Models of the Electrocatalytic Oxygen Evolution Reaction

The electrocatalytic oxygen evolution reaction (OER) supplies the protons and electrons needed to transform renewable electricity into chemicals and fuels. However, the OER is kinetically sluggish; it operates at significant rates only when the applied potential far exceeds the reversible voltage. The origin of this overpotential is hidden in a complex mechanism involving multiple electron transfers and chemical bond making/breaking steps. Our desire to improve catalytic performance has then made mechanistic studies of the OER an area of major scientific inquiry, though the complexity of the reaction has made understanding difficult. While historically, mechanistic studies have relied solely on experiment and phenomenological models, over the past twenty years ab initio simulation has been playing an increasingly important role in developing our understanding of the electrocatalytic OER and its reaction mechanisms. In this Review we cover advances in our mechanistic understanding of the OER, organized by increasing complexity in the way through which the OER is modeled. We begin with phenomenological models built using experimental data before reviewing early efforts to incorporate ab initio methods into mechanistic studies. We go on to cover how the assumptions in these early ab initio simulations─no electric field, electrolyte, or explicit kinetics─have been relaxed. Through comparison with experimental literature, we explore the veracity of these different assumptions. We summarize by discussing the most critical open challenges in developing models to understand the mechanisms of the OER.

Redox dynamics and surface structures of an active palladium catalyst during methane oxidation

Catalysts based on palladium are among the most effective in the complete oxidation of methane. Despite extensive studies and notable advances, the nature of their catalytically active species and conceivable structural dynamics remains only partially understood. Here, we combine operando transmission electron microscopy (TEM) with near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) and density functional theory (DFT) calculations to investigate the active state and catalytic function of Pd nanoparticles (NPs) under methane oxidation conditions. We show that the particle size, phase composition and dynamics respond appreciably to changes in the gas-phase chemical potential. In combination with mass spectrometry (MS) conducted simultaneously with in situ observations, we uncover that the catalytically active state exhibits phase coexistence and oscillatory phase transitions between Pd and PdO. Aided by DFT calculations, we provide a rationale for the observed redox dynamics and demonstrate that the emergence of catalytic activity is related to the dynamic interplay between coexisting phases, with the resulting strained PdO having more favorable energetics for methane oxidation.

Structure Sensitivity of Metal Catalysts Revealed by Interpretable Machine Learning and First-Principles Calculations

The nature of the active sites and their structure sensitivity are the keys to rational design of efficient catalysts but have been debated for almost one century in heterogeneous catalysis. Though the Brønsted-Evans-Polanyi (BEP) relationship along with linear scaling relation has long been used to study the reactivity, explicit geometry, and composition properties are absent in this relationship, a fact that prevents its exploration in structure sensitivity of supported catalysts. In this work, based on interpretable multitask symbolic regression and a comprehensive first-principles data set, we discovered a structure descriptor, the topological under-coordinated number mediated by number of valence electrons and the lattice constant, to successfully address the structure sensitivity of metal catalysts. The database used for training, testing, and transferability investigation includes bond-breaking barriers of 20 distinct chemical bonds over 10 transition metals, two metal crystallographic phases, and 17 different facets. The resulting 2D descriptor composing the structure term and the reaction energy term shows great accuracy to predict the reaction barriers and generalizability over the data set with diverse chemical bonds in symmetry, bond order, and steric hindrance. The theory is physical and concise, providing a constructive strategy not only to understand the structure sensitivity but also to decipher the entangled geometric and electronic effects of metal catalysts. The insights revealed are valuable for the rational design of the site-specific metal catalysts.

Density functional theory methods applied to homogeneous and heterogeneous catalysis: a short review and a practical user guide

The application of density functional theory (DFT) methods in catalysis has been growing fast in the last few decades thanks to both the availability of more powerful high computing resources and the development of new efficient approximations and approaches. DFT calculations allow for the understanding of crucial catalytic aspects that are difficult or even impossible to access by experiments, thus contributing to faster development of more efficient and selective catalysts. Depending on the catalytic system and properties under investigation, different approaches should be used. Moreover, the reliability of the obtained results deeply depends on the approximations involved in both the selected method and model. This review addresses chemists, physicists and materials scientists whose interest deals with the application of DFT-based computational tools in both homogeneous catalysis and heterogeneous catalysis. First, a brief introduction to DFT is presented. Then, the main approaches based on atomic centered basis sets and plane waves are discussed, underlining the main differences, advantages and limitations. Eventually, guidance towards the selection of the catalytic model is given, with a final focus on the evaluation of the energy barriers, which represents a crucial step in all catalytic processes. Overall, the review represents a rational and practical guide for both beginners and more experienced users involved in the wide field of catalysis.

Leveraging Expertise in Thermal Catalysis to Understand Plasma Catalysis

Best practices in testing heterogeneous catalysts are translated to plasma-catalytic experiments. Independent determination of plasma-catalytic and plasma-chemical contributions is essential. Non-porous catalyst particles are preferred because active sites inside sub-micron pores cannot contribute. Temperature variation is needed to determine kinetics, despite the complexity of thermal effects in plasma. Rigorous checks on catalyst deactivation and mass balance are needed. Plasma enhanced reversed reactions should be minimized by keeping conversion low and far from thermodynamic equilibrium, preventing underestimation of the rate of forward reaction. In contrast, plasma-catalytic studies often aim at conversions surpassing thermodynamic equilibrium, not obtaining any information on kinetics. Calculation of catalyst activity per active sites (turn-over-frequency) requires also appropriate characterization to determine the number of active sites. The relationship between kinetics and thermodynamics for plasma-catalysis is discussed using endothermic decomposition of CO2 and exothermic synthesis of ammonia from N2 and H2 as examples. Assuming Langmuir-Hinshelwood and Eley-Rideal mechanisms, the effect of excitation of reactant molecules on activation barriers and surface coverages are discussed, influencing reaction rates. The consequences of reversed reactions are considered. Plasma-catalysis with catalysts applied for thermal catalysis at much higher temperature should be avoided, as adsorbed species are bonded too strongly resulting in low rates.

Demonstrating Pressure Jumping as a Tool to Address the Pressure Gap in High Pressure Photoelectron Spectroscopy of CO and CO2 Hydrogenation on Rh(211)

Operando probing by x-ray photoelectron spectroscopy (XPS) of certain hydrogenation reactions are often limited by the scattering of photoelectrons in the gas phase. This work describes a method designed to partially circumvent this so called pressure gap. By performing a rapid switch from a high pressure (where acquisition is impossible) to a lower pressure we can for a short while probe a "remnant" of the high pressure surface as well as the time dynamics during the re-equilibration to the new pressure. This methodology is demonstrated using the CO2 and the CO hydrogenation reaction over Rh(211). In the CO2 hydrogenation reaction, the remnant surface of a 2 bar pressure shows an adsorbate distribution which favors chemisorbed CHx adsorbates over chemisorbed CO. This contrasts against previous static operando spectra acquired at lower pressures. Furthermore, the pressure jumping method yields a faster acquisition and more detailed spectra than static operando measurements above 1 bar. In the CO hydrogenation reaction, we observe that CHx accumulated faster during the 275 mbar low pressure regime, and different hypotheses are presented regarding this observation.

Fundamental Drivers of Electrochemical Barriers

We find that ion creation and destruction dominate the behavior of electrochemical reaction barriers, through grand-canonical electronic structure calculations of proton deposition on transition metal surfaces. We show that barriers respond to potential in a nonlinear manner and trace this to the continuous degree of electron transfer as an ion is created or destroyed. This explains both Marcus-like curvature and Hammond-like shifts. Across materials, we find the barrier energy to be driven primarily by the charge presented on the surface, which, in turn, is dictated by the native work function, a fundamentally different driving force than in nonelectrochemical systems.

Enhanced CO2 Reactive Capture and Conversion Using Aminothiolate Ligand-Metal Interface

Metallic catalyst modification by organic ligands is an emerging catalyst design in enhancing the activity and selectivity of electrocatalytic carbon dioxide (CO2) reactive capture and reduction to value-added fuels. However, a lack of fundamental science on how these ligand-metal interfaces interact with CO2 and key intermediates under working conditions has resulted in a trial-and-error approach for experimental designs. With the aid of density functional theory calculations, we provided a comprehensive mechanism study of CO2 reduction to multicarbon products over aminothiolate-coated copper (Cu) catalysts. Our results indicate that the CO2 reduction performance was closely related to the alkyl chain length, ligand coverage, ligand configuration, and Cu facet. The aminothiolate ligand-Cu interface significantly promoted initial CO2 activation and lowered the activation barrier of carbon-carbon coupling through the organic (nitrogen (N)) and inorganic (Cu) interfacial active sites. Experimentally, the selectivity and partial current density of the multicarbon products over aminothiolate-coated Cu increased by 1.5-fold and 2-fold, respectively, as compared to the pristine Cu at -1.16 VRHE, consistent with our theoretical findings. This work highlights the promising strategy of designing the ligand-metal interface for CO2 reactive capture and conversion to multicarbon products.

Machine Learning-Based Prediction of Activation Energies for Chemical Reactions on Metal Surfaces

In computational surface catalysis, the calculation of activation energies of chemical reactions is expensive, which, in many cases, limits our ability to understand complex reaction networks. Here, we present a universal, machine learning-based approach for the prediction of activation energies for reactions of C-, O-, and H-containing molecules on transition metal surfaces. We rely on generalized Bronsted-Evans-Polanyi relationships in combination with machine learning-based multiparameter regression techniques to train our model for reactions included in the University of Arizona Reaction database. In our best approach, we find a mean absolute error for activation energies within our test set of 0.14 eV if the reaction energy is known and 0.19 eV if the reaction energy is unknown. We expect that this methodology will often replace the explicit calculation of activation energies within surface catalysis when exploring large reaction networks or screening catalysts for desirable properties in the future.

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.

Stability and reactivity of metal nanoclusters supported on transition metal carbides

Small particles of transition metals (TM) supported on transition metal carbides (TMC) - TM_n@TMC - provide a plethora of design opportunities for catalytic applications due to their highly exposed active centres, efficient atom utilisation and the physicochemical properties of the TMC support. To date, however, only a very small subset of TM_n@TMC catalysts have been tested experimentally and it is unclear which combinations may best catalyse which chemical reactions. Herein, we develop a high-throughput screening approach to catalyst design for supported nanoclusters based on density functional theory, and apply it to elucidate the stability and catalytic performance of all possible combinations between 7 monometallic nanoclusters (Rh, Pd, Pt, Au, Co, Ni and Cu) and 11 stable support surfaces of TMCs with 1 : 1 stoichiometry (TiC, ZrC, HfC, VC, NbC, TaC, MoC and WC) towards CH₄ and CO₂ conversion technologies. We analyse the generated database to unravel trends or simple descriptors in their resistance towards metal aggregate formation and sintering, oxidation, stability in the presence of adsorbate species, and study their adsorptive and catalytic properties, to facilitate the discovery of novel materials in the future. We identify 8 TM_n@TMC combinations as promising catalysts, all of them being new for experimental validation, thus expanding the chemical space for efficient conversion of CH₄ and CO₂.

The importance of a charge transfer descriptor for screening potential CO2 reduction electrocatalysts

It has been over twenty years since the linear scaling of reaction intermediate adsorption energies started to coin the fields of heterogeneous and electrocatalysis as a blessing and a curse at the same time. It has established the possibility to construct activity volcano plots as a function of a single or two readily accessible adsorption energies as descriptors, but also limited the maximal catalytic conversion rate. In this work, it is found that these established adsorption energy-based descriptor spaces are not applicable to electrochemistry, because they are lacking an important additional dimension, the potential of zero charge. This extra dimension arises from the interaction of the electric double layer with reaction intermediates which does not scale with adsorption energies. At the example of the electrochemical reduction of CO₂ it is shown that the addition of this descriptor breaks the scaling relations, opening up a huge chemical space that is readily accessible via potential of zero charge-based material design. The potential of zero charge also explains product selectivity trends of electrochemical CO₂ reduction in close agreement with reported experimental data highlighting its importance for electrocatalyst design.

Machine Learning and Informatics Based Elucidation of Reaction Pathways for Upcycling Model Polyolefin to Aromatics

Catalytic upcycling of plastics results in a complex network of potentially thousands of reactions and intermediates. Manual analysis of such a network using ab initio methods to identify plausible reaction pathways and rate-controlling steps is intractable. Here, we combine informatics-based reaction network generation and machine learning based thermochemistry calculation to identify plausible (nonelementary step) pathways involved in dehydroaromatization of a model polyolefin, n-decane, to form aromatic products. All 78 aromatic molecules found involve a sequence comprising dehydrogenation, β-scission, and cyclization steps (in slightly different order). The plausible flux-carrying pathway depends on the family of reactions that is rate-controlling while the thermodynamic bottleneck is the first dehydrogenation step of n-decane. The adopted workflow is system agnostic and can be applied to understand the overall thermochemistry of other upcycling systems.

Bonds over Electrons: Proton Coupled Electron Transfer at Solid-Solution Interfaces

This Perspective argues that most redox reactions of materials at an interface with a protic solution involve net proton-coupled electron transfer (PCET) (or other cation-coupled ET). This view contrasts with the traditional electron-transfer-focused view of redox reactions at semiconductors, but redox processes at metal surfaces are often described as PCET. Taking a thermodynamic perspective, transfer of an electron is typically accompanied by a stoichiometric proton, much as the chemistry of lithium-ion batteries involves coupled transfers of e^- and Li⁺. The PCET viewpoint implicates the surface-H bond dissociation free energy (BDFE) as the preeminent energetic parameter and its conceptual equivalents, the electrochemical ne^-/nH⁺ potential versus the reversible hydrogen electrode (RHE) and the free energy of hydrogenation, ΔG°_H. These parameters capture the thermochemistry of PCET at interfaces better than electronic parameters such as Fermi energies, electron chemical potentials, flat-band potentials, or band-edge energies. A unified picture of PCET at metal and semiconductor surfaces is presented. Exceptions, limitations, implications, and future directions motivated by this approach are described.

Surface charge as activity descriptors for electrochemical CO2 reduction to multi-carbon products on organic-functionalised Cu

Intensive research in electrochemical CO₂ reduction reaction has resulted in the discovery of numerous high-performance catalysts selective to multi-carbon products, with most of these catalysts still being purely transition metal based. Herein, we present high and stable multi-carbon products selectivity of up to 76.6% across a wide potential range of 1 V on histidine-functionalised Cu. In-situ Raman and density functional theory calculations revealed alternative reaction pathways that involve direct interactions between adsorbed histidine and CO₂ reduction intermediates at more cathodic potentials. Strikingly, we found that the yield of multi-carbon products is closely correlated to the surface charge on the catalyst surface, quantified by a pulsed voltammetry-based technique which proved reliable even at very cathodic potentials. We ascribe the surface charge to the population density of adsorbed species on the catalyst surface, which may be exploited as a powerful tool to explain CO₂ reduction activity and as a proxy for future catalyst discovery, including organic-inorganic hybrids.

Electronic structure and catalytic activity of exsolved Ni on Pd core-shell nanoparticles

This study reports first principles calculations performed to study the electronic structure and catalytic activity of exsolved Ni on Pd core-shell catalysts reported in recent experimental literature. The modification in the electronic and geometric properties of the Ni/Pd bimetallic system as successive layers of Ni are added on top of Pd is systematically investigated using the d-band model as well as the adsorption of O and CO on the surface of these core-shell structures. The results show that the adsorption of O and CO is more favourable on Ni/Pd core-shell catalysts compared to the pure Ni surface. As the dissociation of the O₂ molecule into atomic oxygen and CO oxidation are key steps in metal-catalysed oxidation reactions, we have examined the energetics of O₂ dissociation and CO oxidation reaction over the (111) faces of Ni as well as Ni/Pd structures. Our results suggest that both adsorption and dissociation are easier on Ni/Pd surfaces compared to a simple Ni surface. Unlike O₂ dissociation, we find that CO oxidation is unfavourable on Ni/Pd in comparison to Ni. The energetics of both reactions follow Brønsted-Evans-Polanyi relationships where the activation energy is linearly related to the reaction energy for all surfaces studied here. We found that a single monolayer of Ni on Pd, due to the synergistic effect of geometric and electronic factors, is the most active among the surfaces studied here towards the adsorption and dissociation of O₂. Both adsorption and dissociation become less favourable with an increase in the thickness of the Ni shell in these core-shell catalysts. A close analysis of the results indicates that both strain and ligand effects are active in the improved catalytic activity seen in Ni on Pd catalysts. Quite understandably, the ligand effect is only seen for the single monolayer of Ni on Pd and fades off as we go to two monolayers of Ni. The results reported here help us understand the connections between the electronic structure and catalytic activity of Ni/Pd core-shell nanoparticles, and these insights are expected to be useful in the development of core-shell catalysts.

DFT and microkinetic study of acetylene transformation on Pd(111), M(111) and PdM(111) surfaces (M = Cu, Ag, Au)

Density functional calculations and microkinetic simulations were performed on the transformation network of acetylene on Pd(111), M(111) and PdM(111) (M = Cu, Ag, Au) surfaces. It is demonstrated that the adsorption energies on alloy surfaces linearly correlate with the values on the pure metal surfaces. A good linear relationship between the co-adsorption energies of initial states and transition states is revealed with which the barriers of most elementary steps in the reaction network were estimated. To shed light on the transformation of acetylene, microkinetic simulations were conducted on the network. The results show that CHCH and H are dominant species on the surfaces and CCH, CCH₂ and CCH₃ are the main intermediates. Analysis indicates that introduction of coinage metals into Pd reduces the activity, but promotes the selectivity by lowering the barrier of CHCH₂ → CH₂CH₂. The present work provides a comprehensive overview of acetylene transformation on palladium, coinage metals and their alloy surfaces. The linear relationship of adsorption energies between the component metal and alloy surfaces and usage of the TSS relationship to evaluate barriers for microkinetic simulations are worthy of being further studied and extended to other systems.

Finding Key Factors for Efficient Water and Methanol Activation at Metals, Oxides, MXenes, and Metal/Oxide Interfaces

Activating water and methanol is crucial in numerous catalytic, electrocatalytic, and photocatalytic reactions. Despite extensive research, the optimal active sites for water/methanol activation are yet to be unequivocally elucidated. Here, we combine transition-state searches and electronic charge analyses on various structurally different materials to identify two features of favorable O–H bond cleavage in H₂O, CH₃OH, and hydroxyl: (1) low barriers appear when the charge of H moieties remains approximately constant during the dissociation process, as observed on metal oxides, MXenes, and metal/oxide interfaces. Such favorable kinetics is closely related to adsorbate/substrate hydrogen bonding and is enhanced by nearly linear O–H–O angles and short O–H distances. (2) Fast dissociation is observed when the rotation of O–H bonds is facile, which is favored by weak adsorbate binding and effective orbital overlap. Interestingly, we find that the two features are energetically proportional. Finally, we find conspicuous differences between H₂O/CH₃OH and OH activation, which hints toward the use of carefully engineered interfaces.

Tracing the reactivity of single atom alloys for ethanol dehydrogenation using ab initio simulations

A full DFT parameterized MKM is used to accurately predict the reactivity trend for ethanol dehydrogenation reaction on SAAs.

Discovering surface reaction pathways using accelerated molecular dynamics and network analysis tools

We present an automated method that maps surface reaction pathways with no experimental data and with minimal human interventions. In this method, bias potentials promoting surface reactions are applied to enable statistical samplings of the surface reaction within the timescale of ab initio molecular dynamics (MD) simulations, and elementary reactions are extracted automatically using an extension of the method constructed for gas- or liquid-phase reactions. By converting the extracted elementary reaction data to directed graph data, MD trajectories can be efficiently mapped onto reaction pathways using a network analysis tool. To demonstrate the power of the method, it was applied to the steam reforming of methane on the Rh(111) surface and to propane reforming on the Pt(111) and Pt₃Sn(111) surfaces. We discover new energetically favorable pathways for both reactions and reproduce the experimentally-observed materials-dependence of the surface reaction activity and the selectivity for the propane reforming reactions.

We present an automated method that maps surface reaction pathways with no experimental data and with minimal human interventions.

Direct conversion of CO2 to a jet fuel over CoFe alloy catalysts

The direct conversion of carbon dioxide (CO₂) using green hydrogen is a sustainable approach to jet fuel production. However, achieving a high level of performance remains a formidable challenge due to the inertness of CO₂ and its low activity for subsequent C–C bond formation. In this study, we prepared a Na-modified CoFe alloy catalyst using layered double-hydroxide precursors that directly transforms CO₂ to a jet fuel composed of C₈–C₁₆ jet-fuel-range hydrocarbons with very high selectivity. At a temperature of 240°C and pressure of 3 MPa, the catalyst achieves an unprecedentedly high C₈–C₁₆ selectivity of 63.5% with 10.2% CO₂ conversion and a low combined selectivity of less than 22% toward undesired CO and CH₄. Spectroscopic and computational studies show that the promotion of the coupling reaction between the carbon species and inhibition of the undesired CO₂ methanation occur mainly due to the utilization of the CoFe alloy structure and addition of the Na promoter. This study provides a viable technique for the highly selective synthesis of eco-friendly and carbon-neutral jet fuel from CO₂.

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An alloy is developed for the direct CO₂ hydrogenation to jet-fuel-range hydrocarbons

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The selectivity of the hydrocarbons (63.5%) exceeds the theoretical maximum value

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The CoFe alloy is the active phase in the coupling reaction between surface carbons

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The CoFe alloy is a highly efficient catalyst in the presence of a sodium promoter

Interpretable Machine Learning of Chemical Bonding at Solid Surfaces

Understanding the nature of chemical bonding and its variation in strength across physically tunable factors is important for the development of novel catalytic materials. One way to speed up this process is to employ machine learning (ML) algorithms with online data repositories curated from high-throughput experiments or quantum-chemical simulations. Despite the reasonable predictive performance of ML models for predicting reactivity properties of solid surfaces, the ever-growing complexity of modern algorithms, e.g., deep learning, makes them black boxes with little to no explanation. In this Perspective, we discuss recent advances of interpretable ML for opening up these black boxes from the standpoints of feature engineering, algorithm development, and post hoc analysis. We underline the pivotal role of interpretability as the foundation of next-generation ML algorithms and emerging AI platforms for driving discoveries across scientific disciplines.

Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO2 Hydrogenation on Ni(111)

Automatic mechanism generation is used to determine mechanisms for the CO2 hydrogenation on Ni(111) in a two-stage process while considering the correlated uncertainty in DFT-based energetic parameters systematically. In a coarse stage, all the possible chemistry is explored with gas-phase products down to the ppb level, while a refined stage discovers the core methanation submechanism. Five thousand unique mechanisms were generated, which contain minor perturbations in all parameters. Global uncertainty assessment, global sensitivity analysis, and degree of rate control analysis are performed to study the effect of this parametric uncertainty on the microkinetic model predictions. Comparison of the model predictions with experimental data on a Ni/SiO2 catalyst find a feasible set of microkinetic mechanisms within the correlated uncertainty space that are in quantitative agreement with the measured data, without relying on explicit parameter optimization. Global uncertainty and sensitivity analyses provide tools to determine the pathways and key factors that control the methanation activity within the parameter space. Together, these methods reveal that the degree of rate control approach can be misleading if parametric uncertainty is not considered. The procedure of considering uncertainties in the automated mechanism generation is not unique to CO2 methanation and can be easily extended to other challenging heterogeneously catalyzed reactions.

Infusing theory into deep learning for interpretable reactivity prediction

Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties.

An evaluation of solvent effects and ethanol oxidation

Understanding liquid-metal interfaces in catalysis is important, as the liquid can speed up surface reactions, increase the selectivity of products, and open up new favorable reaction pathways. In this work we modeled using density functional theory various steps in ethanol oxidation/decomposition over Rh(111). We considered implicit (continuum), explicit, and hybrid (implicit combined with explicit) solvation approaches, as well as two solvents, water and ethanol. We focused on modeling adsorption steps, as well as C-C/C-H bond scission and C-O bond formation reactions. Implicit solvation had very little effect on adsorption and reaction free energies. However, using the explicit and hybrid models, some free energies changed significantly. Furthermore, ethanol solvent had a more considerable impact than water solvent. We observed that preferred reaction pathways for C-C scission changed depending on the solvation model and solvent choice (ethanol or water). We also applied the bond-additivity solvation method to calculate heats of adsorption. Heats of adsorption and reaction using the bond-additivity model followed the same trends as the other solvation models, but were ∼1.1 eV more endothermic. Our work highlights how different solvation approaches can influence analysis of the oxidation/decomposition of organic surface species.

Microkinetic Modeling: A Tool for Rational Catalyst Design

The design of heterogeneous catalysts relies on understanding the fundamental surface kinetics that controls catalyst performance, and microkinetic modeling is a tool that can help the researcher in streamlining the process of catalyst design. Microkinetic modeling is used to identify critical reaction intermediates and rate-determining elementary reactions, thereby providing vital information for designing an improved catalyst. In this review, we summarize general procedures for developing microkinetic models using reaction kinetics parameters obtained from experimental data, theoretical correlations, and quantum chemical calculations. We examine the methods required to ensure the thermodynamic consistency of the microkinetic model. We describe procedures required for parameter adjustments to account for the heterogeneity of the catalyst and the inherent errors in parameter estimation. We discuss the analysis of microkinetic models to determine the rate-determining reactions using the degree of rate control and reversibility of each elementary reaction. We introduce incorporation of Brønsted-Evans-Polanyi relations and scaling relations in microkinetic models and the effects of these relations on catalytic performance and formation of volcano curves are discussed. We review the analysis of reaction schemes in terms of the maximum rate of elementary reactions, and we outline a procedure to identify kinetically significant transition states and adsorbed intermediates. We explore the application of generalized rate expressions for the prediction of optimal binding energies of important surface intermediates and to estimate the extent of potential rate improvement. We also explore the application of microkinetic modeling in homogeneous catalysis, electro-catalysis, and transient reaction kinetics. We conclude by highlighting the challenges and opportunities in the application of microkinetic modeling for catalyst design.

CO2 reduction and ethane dehydrogenation on transition metal catalysts: mechanistic insights, reactivity trends and rational design of bimetallic alloys

Reactivity trends of transition metal catalysts, studied for the ethane dehydrogenation reaction using CO₂ as a mild oxidant.

Understanding the origin of structure sensitivity in hydrodechlorination of trichloroethylene on a palladium catalyst

Mechanistic understanding on the origin of structure sensitivity in the hydrodechlorination of trichloroethylene.