A dimer method for finding saddle points on high dimensional potential surfaces using only first derivatives

Inverted Trends of the Brønsted-Evans-Polanyi Relation in N2 Dissociation Originated from a Bonding-Dependent Adsorption Mechanism

The search for efficient Haber-Bosch catalysts toward ammonia production under mild conditions is never-ending, which is greatly limited by the Brønsted-Evans-Polanyi (BEP) relationship. Great efforts have been put into optimizing the BEP relations and achieving the Sabatier optimum, which requires a balance between the dissociation and hydrogenation of nitrogen. However, challenges in this field inspire us to believe that completely breaking the linear BEP relations is indeed the final target although out of sight in such a holy grail reaction. Here, based on the first-principles calculations, we discover inverted trends of BEP relation of N2 dissociation to approach the kinetic optimum of ammonia synthesis on Fe-based single-atom alloys. It is found that the adsorption characteristic of N-N transition states follows the 10-electron count rule, while that of the final states mimics the d-band model, which accounts for the inversion. Crystal orbital Hamiltonian populations (COHP) and Bader charge analysis further corroborate that a bonding-dependent adsorption mechanism lies at the root of the inverted trends of the BEP relation. Our finding not only paves the way for the milder Haber-Bosch process but also promotes explorations of breaking the linear BEP relations of the critical steps in various chemical reactions.

Beyond Numerical Hessians: Higher-Order Derivatives for Machine Learning Interatomic Potentials via Automatic Differentiation

The development of machine learning interatomic potentials (MLIPs) has revolutionized computational chemistry by enhancing the accuracy of empirical force fields while retaining a large computational speed-up compared to first-principles calculations. Despite these advancements, the calculation of Hessian matrices for large systems remains challenging, in particular because analytical second-order derivatives are often not implemented. This necessitates the use of computationally expensive finite-difference methods, which can furthermore display low precision in some cases. Automatic differentiation (AD) offers a promising alternative to reduce this computational effort and makes the calculation of Hessian matrices more efficient and accurate. Here, we present the implementation of AD-based second-order derivatives for the popular MACE equivariant graph neural network architecture. The benefits of this method are showcased via a high-throughput prediction of heat capacities of porous materials with the MACE-MP-0 foundation model. This is essential for precisely describing gas adsorption in these systems and was previously possible only with bespoke ML models or expensive first-principles calculations. We find that the availability of foundation models and accurate analytical Hessian matrices offers comparable accuracy to bespoke ML models in a zero-shot manner and additionally allows for the investigation of finite-size and rounding errors in the first-principles data.

Automated Pynta-Based Curriculum for ML-Accelerated Calculation of Transition States

Microkinetic models (MKMs) are widely used within the computational heterogeneous catalysis community to investigate complex reaction mechanisms, to rationalize experimental trends, and to accelerate the rational design of novel catalysts. However, constructing these models requires computationally expensive and manually tedious density functional theory (DFT) calculations for identifying transition states for each elementary reaction within the MKM. To address these challenges, we demonstrate a novel protocol that uses the open-source kinetics workflow tool Pynta to automate the iterative training of a reactive machine learning potential (rMLP). Specifically, using the silver-catalyzed partial oxidation of methanol as a prototypical example, we first demonstrate our workflow by training an rMLP to accelerate the parallel calculation of DFT-quality transition states for all 53 reactions, achieving a 7× speedup compared to a DFT-only strategy. Detailed analysis of our training curriculum reveals the shortcomings of using an adaptive sampling scheme with a single rMLP model to describe all reactions within the MKM simultaneously. We show that these limitations can be overcome using a balanced "reaction class" approach that uses multiple rMLP models, each describing a single class of similar transition states. Finally, we demonstrate that our Pynta-based workflow is also compatible with large pretrained foundational models. For example, by fine-tuning a top-performing graph neural network potential trained on the OC20 dataset, we observe an impressive 20× speedup with an 89% success rate in identifying transition states. This work highlights the synergistic potential of integrating automated tools with machine learning to advance catalysis research.

Diffusive first-order phase transition: nucleation, growth and coarsening in solids

The phenomena of nucleation and growth, which fall into the category of first-order phase transitions, are of great importance. They are present everywhere in our daily lives. They enable us to understand and model a vast number of phenomena, from the formation of raindrops, to the gelling of polymers, the evolution of a virus population and the formation of galaxies. Surprisingly, this whole range of phenomena can be described by two seemingly antagonistic approaches: classical nucleation theory, which highlights the atomistic approach of the diffusion process, and the phase-field (PF) approach, which erases the discrete nature of the diffusion process. Although there is an huge quantity of articles and review papers dealing with the problem of first-order phase transition, the subject is so important and vast that it is very difficult to provide nowadays exhaustive syntheses on the subject. The revival over the past 20 years in the condensed matter world of PF approaches such as PF crystal, or the recent development of optimization methods such as gentle ascend dynamics, as well as the emergence of atom probe tomography, have enabled us to better understand the links between these antagonistic approaches, and above all to provide new experimental results to test the limits of both. This renewal has motivated the writing of this review, both to take stock of current knowledge on these two approaches. This review has two distinct objectives: summarizing generic previous models applies to discuss the nucleation, the growth and the coarsening processes. Despite some reviews already exist on these different subject, few of them present the different logical links between these models and their limitations, unifying them within the framework of the theory of macroscopic fluctuations, which has been developed over the last 20 years. In particular, we present the extension of the Cahn-Hilliard formalism to model the nucleation and growth process and we discuss the relevance of the notion of pseudo-spinodal and discuss. Such an extension allows interpreting experiments performed fat from the solubility limit and the spinodal line. Finally, this work proposes some clues to make this unified approach more predictive.

Machine learning and DFT database for C-H dissociation on single-atom alloy surfaces in methane decomposition

Methane decomposition using single-atom alloy (SAA) catalysts, known for uniform active sites and high selectivity, significantly enhances hydrogen production efficiency without CO2 emissions. This study introduces a comprehensive database of C-H dissociation energy barriers on SAA surfaces, generated through machine learning (ML) and density functional theory (DFT). First-principles DFT calculations were utilized to determine dissociation energy barriers for various SAA surfaces, and ML models were trained on these results to predict energy barriers for a wide range of SAA surface compositions. The resulting dataset, comprising 10,950 entries with descriptors and energy barriers, as main predictive outcomes, has been validated against existing DFT calculations confirming the reliability of the ML predictions. This dataset provides valuable insights into the catalytic mechanisms of SAAs and supports the development of efficient, low-emission hydrogen production technologies. All data and computational tools are publicly accessible for further advancements in catalysis and sustainable energy solutions.

Highly stable rare earth YS2 and ScS2 monolayers for potassium-ion batteries: first-principles calculations

Most currently reported anode materials for potassium-ion batteries (KIBs) face a significant trade-off between high potassium capacity and stability, limiting their practical applications. It is widely recognized that reversible potassium intercalation during potassiation and depotassiation processes offers a promising approach to achieving long-term cycle stability. In this study, we conducted a comprehensive investigation of the MS2 monolayer family as anodes for K-ion batteries, utilizing density functional theory (DFT) computations. We demonstrated that the lowest unoccupied states (ELUS) of MS2 monolayers can serve as a simple yet effective descriptor for evaluating potassium adsorption ability. It was revealed that a lower ELUS of the material can lead to more energetically favourable electron occupation, resulting in stronger K adsorption. The proposed potassiation mechanisms were largely dependent on the delicate competition between the K-MS2 interaction (Eads) and the M-S bonding interaction (ΔHf) within the MS2 structure. Our computations indicated that most of the MS2 monolayers (except for CoS2, NiS2, and PdS2) could suppress the conversion reaction after K-ion insertion owing to the less electrovalent K-S bond. By evaluating the theoretical capacities, diffusion barriers, and electronic characteristics, the rare earth Sc- and Y-mediated MS2 monolayers were identified as the most promising intercalation candidates for KIB anodes with maximum theoretical capacities of 509.28 and 461.09 mA h g-1 and exceptionally low ion diffusion barriers of 0.11 eV and 0.12 eV, respectively. This study provides an effective strategy for designing stable and high-performance electrodes for potassium-ion batteries, thereby advancing the development of next-generation energy storage systems.

Enhanced plasma-driven H2S removal from natural gas via TiO2-coated dielectric surface modification

The efficient removal of H2S impurities from natural gas is critical for improving gas quality and reducing maintenance costs. This study explores the integration of dielectric barrier discharge (DBD) plasma with TiO2 to decompose H2S effectively. Results show that the presence of TiO2 significantly improve H2S removal and energy efficiency compared with the plasma-only condition. However, the TiO2-coated system achieves a much higher H2S removal rate (5.4 mmol/h/gTiO2), which was 27 times that of TiO2-packed system, minimizing TiO2 usage. Moreover, coated TiO2 inhibits methane conversion, preserving the primary components of natural gas. Discharge analysis reveals that packing TiO2 increases the reduced electric field and enhances mean electron energy, while coating further promotes filamentary discharge. Density functional theory (DFT) calculations confirm that defect-rich TiO2, formed under plasma conditions, plays a crucial role in facilitating H2S decomposition. A plausible reaction pathway for plasma-driven H2S decomposition with coated TiO2 is proposed. This study demonstrates the potential of DBD-coupled coated catalyst technology for efficient H2S removal, offering a scalable solution for industrial gas purification.

Controlling Reaction Pathways of Ethylene Hydroformylation Using Isolated Bimetallic Rhodium-Cobalt Sites

Designing efficient ligand-free heterogeneous catalysts for ethylene hydroformylation to produce C3 oxygenates is of importance for both fundamental research and practical applications, but it is often hindered by insufficient catalytic activity and selectivity. This work designs isolated rhodium-cobalt (Rh-Co) sites confined within a ZSM-5 zeolite to enhance ethylene hydroformylation rates and selectivity while maintaining catalyst stability. By adjusting the Co/Al ratio in Co-ZSM-5, different sizes of Co are formed; subsequent Rh introduction produces isolated Rh1Cox clusters with different Rh-Co coordination numbers (CNs). In-situ characterizations and density functional theory calculations reveal that a Rh-Co CN of 3, corresponding to an isolated Rh1Co3 site, provides optimal bindings to reaction intermediates and thus achieves the highest hydroformylation rates among supported Rh-based catalysts. This study demonstrates the role of coordination-tuning via a secondary metal in effectively controlling the reaction pathway over single Rh atom catalysts.

Computational framework for discovery of degradation mechanisms of organic flow battery electrolytes

The stability of organic redox-active molecules is a key challenge for the long-term viability of organic redox flow batteries (ORFBs). Electrolyte degradation leads to capacity fade, reducing the efficiency and lifespan of ORFBs. To systematically investigate degradation mechanisms, we present a computational framework that automates the exploration of degradation pathways. The approach integrates local reactivity descriptors to generate reactive complexes and employs a single-ended process search to discover elementary reaction steps, including transition states and intermediates. The resulting reaction network is iteratively refined with heuristics and human-guided validation. The framework is applied to study the degradation mechanisms of quinone- and quinoxaline-based electrolytes under acidic and basic aqueous conditions. The predicted reaction pathways and degradation products align with experimental observations, highlighting key degradation modes such as Michael addition, disproportionation, dimerization, and electrochemical transformation. The framework provides a valuable tool for in silico screening of stable electrolyte candidates and guiding the molecular design of next-generation ORFBs.

Low-Temperature Direct Oxidation of Propane to Propylene Oxide Using Supported Subnanometer Cu Clusters

Propylene oxide, a key commodity of the chemical industry for a wide range of consumer products, is synthesized through sequential propane dehydrogenation and epoxidation reactions. However, the lack of a direct catalytic route from propane to propylene oxide reduces efficiency and represents a major challenge for catalysis science. Herein, we report the discovery of a highly active and selective catalyst, made of alumina-supported subnanometer copper clusters, which can directly convert propane to propylene oxide at temperatures as low as 150 °C. Moreover, at higher temperatures, on the same catalysts, the selectivity is switched to propylene. Accompanying theoretical calculations indicate that partially oxidized and/or hydroxylated clusters have low activation energies for both propane dehydrogenation and propylene epoxidation pathways, enabling direct conversion with very high selectivity for propylene oxide. The discovery of a low-temperature catalyst that can convert propane directly to propylene oxide provides an important opportunity for the development of energy-efficient and economic catalysts for this industrially critical process. Similarly, when operating at higher temperatures, these catalysts are posed as potent oxidative dehydrogenation catalysts.

Intentional Formation of Persistent Surface Redox Mediators by Adsorption of Polyconjugated Carbonyl Complexes to Pd Nanoparticles

Adsorbing polyconjugated carbonyl and aromatic species to Pd nanoparticles forms persistent intermediates that mediate reactions between hydrogen and oxygen-derived species. These surface redox mediators form in situ and increase selectivities toward H2O2 formation (∼65-85%) compared to unmodified Pd nanoparticles (∼45%). Infrared spectroscopy, temperature-programmed oxidation measurements, and ab initio calculations show that these species adsorb irreversibly to Pd surfaces and persist over extended periods of catalysis. Combined rates and kinetic isotope effect measurements and simulations suggest that carbonyl groups of bound organics react heterolytically with hydrogen to form partially hydrogenated oxygenated complexes. Subsequently, these organic species transfer proton-electron pairs to O2-derived surface species via pathways that favor H2O2 over H2O formation on Pd nanoparticles. Computational and experimental measurements show redox pathways mediated by partially hydrogenated carbonyl species form H2O2 with lower barriers than competing processes while also obstructing O-O bond dissociation during H2O formation. For example, adsorption and hydrogenation of hexaketocyclohexane on Pd forms species that react with oxygen with high H2O2 selectivities (85 ± 8%) for 130 h on stream in flowing water without additional promoters or cosolvents. These paths resemble the anthraquinone auto-oxidation process (AAOP) used for industrial H2O2 production. These surface-bound species form partially hydrogenated intermediates that mediate H2O2 formation with high rates and selectivities, comparable to AAOP but on a single catalytic nanoparticle in pure water without organic solvents or multiunit reaction-separation chains. The molecular insights developed herein provide strategies to avoid organic solvents in selective processes and circumvent their associated process costs and environmental impacts.

Electrocatalytic CO2 Reduction Empowered by 2D Hexagonal Transition Metal Borides

Electrocatalysis holds immense promise for producing high-value chemicals and fuels through the carbon dioxide reduction reaction (CO2RR), advancing global sustainability and carbon neutrality. However, conventional electrocatalysts based on transition metals are often limited by significant overpotentials. Since the discovery of the first hexagonal MAB (h-MAB) phase, Ti2InB2, and its 2D derivative in 2019, 2D hexagonal transition metal borides (h-MBenes) have emerged as promising candidates for various electrochemical applications. This study presents the first theoretical investigation into the CO2RR catalytic properties of pristine h-MBenes (h-MB) and their ─O (h-MBO) and ─OH (h-MBOH) terminated counterparts, focusing on metals such as Sc, Ti, V, Zr, Nb, Hf, and Ta. These results reveal while h-MB and h-MBO exhibit poor catalytic performance due to overly strong or weak interactions with CO2, h-MBOH shows great promise. Notably, ScBOH, TiBOH, and ZrBOH display exceptionally low limiting potentials (UL) of -0.46, -0.53, and -0.64 V, respectively. These findings uncover the unique role of ─OH in tuning the electronic properties of h-MBenes, thereby optimizing intermediate adsorption, which prevents excessive binding and enhances catalytic efficiency. This research offers valuable insights into the potential of h-MBenes as highly efficient CO2RR catalysts, underscoring their versatility and significant prospects for electrochemical applications.

First-Principles Investigation of Hydroxyl Species Formation on β-MnO2(110) for Catalytic Oxidation Applications

Hydroxyl (OH) species play a critical role in several oxidative catalysis processes, including the oxidation of 5-hydroxymethylfurfural (HMF) to produce valuable compounds like 2,5-furandicarboxylic acid (FDCA). High OH coverage on metal oxide surfaces significantly enhanced catalytic activity. Herein, we investigated OH coverage on the β-MnO2(110) surface generated through the decomposition of oxidant molecules (O2, H2O2, and tert-butyl hydroperoxide, TBHP) using density functional theory (DFT) calculations and ab initio thermodynamic modeling. We studied the kinetics and thermodynamics aspects of OH formation pathways, focusing on direct O-O and C-O bond cleavages and reactions with H2O, both in gas and solvent environments. Computations reveal that TBHP and H2O2 exhibit lower dissociation barriers and favorable thermodynamics than O2, yielding higher OH coverage under relevant reaction conditions. Phase diagrams constructed from thermodynamic models reveal that TBHP maintains high OH coverage across a broader temperature range, suggesting its potential as an efficient oxidant for catalytic applications. These insights support the development of β-MnO2 catalysts tailored for oxidation processes by guiding oxidant selection and reaction conditions.

Quantum Calculations of Hydrogen Absorption and Diffusivity in Bulk CeO2

CeO2 (ceria) is an attractive material for heterogeneous catalysis applications involving hydrogen due to its favorable redox activity combined with its relative impermeability to hydrogen ions and molecules. However, to date, many bulk ceria/hydrogen properties remain unresolved in part due to a scarcity of experimental data combined with quantum calculation results that vary according to the approach used. In this regard, we have conducted a series of density functional theory (DFT) calculations utilizing generalized gradient (GGA), metaGGA, and hybrid functionals as well as several corrections for electronic correlations, applied to a number of properties regarding hydrogen in bulk stoichiometric CeO2. Our calculations place reasonable bounds on the lattice constants, band gaps, hydrogen absorption energies, and O-H bond vibrational frequencies that can be determined by DFT. In addition, our results indicate that the activation energy barriers for hydrogen bulk diffusion are uniformly low (<0.15 eV) for the calculation parameters probed here and that, in general, the effect of hydrogen tunneling is small at ambient temperatures. Our study provides a recipe to determine fundamental physical chemical properties of Ce-O-H interactions while also determining realistic ranges for diffusion kinetics. This can facilitate the determination of future coarse-grained models that will be able to guide and elucidate experimental efforts in this area.

On-surface C-C coupling reactivity of carbon atoms in halogenated azulene

Azulene molecule, consisting of a pair of five and seven-membered rings, represents a promising precursor for the on-surface synthesis of nonbenzenoid, nonalternant carbon nanostructures with exotic properties. However, controlling the selective C-C coupling between azulene molecules remains elusive, undermining the structural uniformity of the attained carbon nanostructures. Here, we report that the on-surface C-C coupling reactivity of different carbon atom sites in azulene relies on the spatial distribution of its frontier orbitals. By performing surface reactions of a tribrominated azulene molecule on Au(111), the probability of C-C coupling between carbon atoms at different sites of azulene has been revealed by scanning tunneling microscopy and non-contact atomic force microscopy. These findings are in accordance with the density functional theory-calculated energy barriers for the corresponding C-C coupling reaction steps.

Temperature-dependent pathways in carbon dioxide electroreduction

Temperature affects both the thermodynamics of intermediate adsorption and the kinetics of elementary reactions. Despite its extensive study in thermocatalysis, temperature effect is typically overlooked in electrocatalysis. This study investigates how electrolyte temperature influences CO2 electroreduction over Cu catalysts. Theoretical calculations reveal the significant impact of temperature on *CO and *H intermediate adsorption thermodynamics, water microenvironment at the electrode surface, and the electron density and covalent property of the C-O bond in the *CH-COH intermediate, crucial for the reaction pathways. The theoretical calculations are strongly verified by experimental results over different Cu catalysts. Faradaic efficiency (FE) toward multicarbon (C2+) products is favored at low temperatures. Cu nanorod electrode could achieve a [Formula: see text] value of 90.1% with a current density of ∼400 mA cm-2 at -3 °C. [Formula: see text] and [Formula: see text] show opposite trends with decreasing temperature. The [Formula: see text] ratio can decrease from 1.86 at 40 °C to 0.98 at -3 °C.

An environmentally adaptive gold single-atom catalyst with variable valence states

Single-atom catalysts revolutionize catalysis by maximizing atomic efficiency and enhancing reaction specificity, offering high activity and selectivity with minimal material usage, which is crucial for sustainable processes. However, the unique properties that distinguish single-atom catalysts from other forms, including bulk and nanoparticle catalysts, as well as the physical mechanisms behind their high activity and selectivity, remain unclear, limiting their broader application. Here, through first-principles calculations, we have identified an environmentally adaptive gold single-atom catalyst on a CeO2(111) surface capable of adjusting its valence state in response to different environmental conditions. This adaptability enables the catalyst to simultaneously maintain high stability and activity. In a CO gas atmosphere, numerous oxygen vacancies form on the CeO2(111) surface, where Au single atoms stably adsorb, exhibiting a negative oxidation state that deactivates the catalyst. In an O2 atmosphere, these vacancies are filled, causing the Au single atoms to adsorb onto lattice oxygen and become oxidized to a positive oxidation state, thereby reactivating the catalyst. Under CO oxidation reaction conditions, the Au single atoms oscillate between these positive and negative oxidation states, effectively facilitating the CO oxidation process. These findings provide new insights into the unique properties and high performance of single-atom catalysts, contributing to a better understanding and utilization of these catalysts in various applications.

Fine-Tuning Graph Neural Networks via Active Learning: Unlocking the Potential of Graph Neural Networks Trained on Nonaqueous Systems for Aqueous CO2 Reduction

Graph neural networks (GNNs) have revolutionized catalysis research with their efficiency and accuracy in modeling complex chemical interactions. However, adapting GNNs trained on nonaqueous data sets to aqueous systems poses notable challenges due to intricate water interactions. In this study, we proposed an active learning-based fine-tuning approach to extend the applicability of GNNs to aqueous environments. The geometry optimization and transition state search workflows are designed to reduce computational costs while maintaining DFT-level accuracy. Applied to the CO2 reduction reaction, the workflow delivers a 2-3-fold acceleration in geometry optimization through a relaxed force threshold combined with DFT refinement. The versatility of the transition state search algorithm was demonstrated on key C-C coupling pathways, pinpointing *CO-*COH as the most energetically favorable pathway in aqueous systems of Cu and Cu-based Ag, Au, and Zn alloys. The Brønsted-Evans-Polanyi relationship remains robust under water-induced fluctuations, with alloyed metals such as Al, Ga, and Pd, along with Ag, Au, and Zn, exhibiting coupling efficiency comparable to that of Cu. Additionally, perturbation-based training on forces and energies extends the application of GNNs to aqueous ab initio molecular dynamics simulations, enabling efficient modeling of dynamical trajectories. This work presents novel approaches to adapting nonaqueous models for application in aqueous systems, highlighting GNNs' potential in solvated environments and laying a foundation for accelerating predictions of catalytic mechanisms under realistic conditions.

Proper aggregation of Pt is beneficial for the epoxidation of styrene by O2 over Ptx/γ-Al2O3 catalysts

The dispersion of metal catalysts has multiple effects on catalytic performance, and higher dispersions do not necessarily imply better performance. Herein, we report the epoxidation reaction of styrene over supported platinum catalysts as an example. Compared with the Pt1/γ-Al2O3 catalyst, the Ptn/γ-Al2O3 catalyst with a larger Pt cluster size showed a much better performance. Combining the results of various characterizations and density functional theory calculations, Ptn/γ-Al2O3 was found to be more favorable for oxygen adsorption and activation to generate singlet oxygen species, further promoting the styrene oxidation reaction to styrene oxide in terms of kinetics. In contrast the metallic center of Pt1 in Pt1/γ-Al2O3 was too small to efficiently activate the diatomic oxygen molecule. These insights provide valuable guidance for designing high-performance metal catalysts.

Integrating photochemical and photothermal effects for selective oxidative coupling of methane into C2+ hydrocarbons with multiple active sites

The direct photocatalytic oxidation of methane to value-added chemicals has garnered considerable interest in recent years. However, achieving high productivity while maintaining high selectivity at an appreciable methane conversion rate remains a formidable challenge. Here, we present photochemically-triggered and photothermally-enhanced oxidative coupling of methane to multi-carbon C2+ alkanes over an Au and CeO2 nanoparticle-decorated ZnO photocatalyst, which exhibits a record-breaking C2+ production rate of 17,260 μmol g-1 h-1 with ~90% C2+ selectivity under wide-spectrum light irradiation without a secondary source of heating. Comprehensive characterizations and computational studies reveal that CH4 activation is a photochemical reaction initiated by ultraviolet light-excited ZnO, and the introduction of CeO2 substantially enhances the activation of CH4 and O2 due to the cooperative interaction between Au and CeO2. Concurrently, Au nanoparticles capture visible and near-infrared light to generate localized heating, which greatly promotes the subsequent desorption of produced methyl radical for C-C coupling prior to undergoing further undesired overoxidation.

Light-driven propane dehydrogenation by a single-atom catalyst under near-ambient conditions

Propane dehydrogenation is an energy-intensive industrial reaction that requires high temperatures (550-750 °C) to overcome thermodynamic barriers. Here we overcome these limits and demonstrate that near-ambient propane dehydrogenation can be achieved through photo-thermo-catalysis in a water-vapour environment. We reduce the reaction temperature to 50-80 °C using a single-atom catalyst of copper supported on TiO2 and a continuous-flow fixed-bed reactor. The mechanism differs from conventional propane dehydrogenation in that hydrogen is produced from the photocatalytic splitting of water vapour, surface-bound hydroxyl radicals extract propane hydrogen atoms to form propylene without over-oxidation, and water serves as a catalyst. This route also works for the dehydrogenation of other small alkanes. Moreover, we demonstrate sunlight-driven water-catalysed propane dehydrogenation operating at reaction temperatures as low as 10 °C. We anticipate that this work will be a starting point for integrating solar energy usage into a wide range of high-temperature industrial reactions.

Electrocatalytic Reduction of CO2 to Long-Chain Hydrocarbons: Investigating the Asymmetric C-C Coupling Mechanism on Pd3Au Catalysts

The electrochemical reduction of CO2 to high-energy-density hydrocarbons is pivotal for addressing energy and environmental challenges. Understanding the mechanisms underlying the conversion of CO2 to long-chain hydrocarbons is both crucial and complex. In this study, we employed density functional theory (DFT) calculations to investigate the C-C coupling mechanisms responsible for the formation of C2-C4 products on Pd3Au catalysts. Our findings highlight the sequential formation of C2-C1 bonds via asymmetric C-C coupling as a critical pathway for generating C3 product, which is essential for controlling product selectivity. Detailed analysis of coupling reactions involving CCH2* and CCH3* intermediates with various C1 species indicates that these asymmetric coupling events are energetically favorable and play a decisive role in the selective assembly of carbon atoms into longer hydrocarbon chains. Moreover, we propose a mechanism wherein the asymmetric coupling of C1-C3 intermediate leads to a diverse array of C4 products. This process underscores the importance of CH2 group coupling with C3 intermediates in forming long-chain hydrocarbons. Our work provides valuable insights into optimizing catalyst design for improved selectivity towards higher hydrocarbons in CO2 reduction processes.

Unlocking the Role of C Doping in a RuO2 Matrix in CO2 Methanation from a Combined Theoretical and Experimental Approach

A new type of ruthenium-based catalyst, labeled RuO x C y @C, consisting of a combination of metallic ruthenium (Ru0), ruthenium oxide (RuO2), and a ruthenium oxycarbonate phase (RuO2C y ) formed by interstitial carbon doped into RuO2, has been recently reported for low-temperature CO2 methanation. Its catalytic activity and long-term stability depend on two competing processes that take place under reaction conditions: RuO2 reduction with H2 to form inactive Ru0 nanoparticles, and C diffusion into RuO2 to form the active oxycarbonate phase. A combination of experimental and computational techniques is applied in this work to investigate the relative rate of both processes in order to identify possible modifications in the catalyst composition that might improve the overall catalytic performance.

From Single Atoms to Clusters: Unraveling the Structural Evolution of Pt/CeO2 for Enhanced CO Oxidation

The structural evolution of catalysts and the identification of active sites are critical yet challenging aspects of heterogeneous reactions. In this work, we investigate the structural evolution of Pt/CeO2 catalysts during CO oxidation by using theoretical calculations, focusing on the influence of initial catalyst states on the resulting active sites and reactivity. Our findings reveal that under the reaction conditions, single Pt atoms gradually aggregate into Pt clusters. When single Pt atoms are substituted for surface Ce atoms (Ptin), the resulting small clusters (Ptn) are exclusively formed based on Ptin. However, when both Ptin and surface-adsorbed Pt atoms (Ptad) coexist, additional small surface-adsorbed clusters (Ptnad) are generated. An increase in the Ptad/Ptin ratio leads to a higher proportion of clusters at the active sites, which correlates with enhanced CO oxidation activity as the number of clusters increases. This study underscores the importance of understanding catalyst evolution and active site dynamics under the reaction conditions, providing theoretical insights for the rational design of more efficient catalysts.

Mechanistic Insights on Coverage-Dependent Selectivity Limitations in Vinyl Acetate Synthesis

Developing improved catalysts for sustainable chemical processes often involves understanding atomistic origins of catalytic activity, selectivity, and stability. Using density functional theory and steady-state kinetic analyses, we probe the elementary steps that form decomposition products that limit selectivity in vinyl acetate (VA) synthesis on Pd surfaces covered with acetate species. Acetate formation and coupling with ethylene control the VA formation catalytic cycle and steady-state coverage, but acetate and ethylene can separately decompose to form CO2. Both decompositions involve initial C-H activations at acetate vacancies, followed by additional C-H activations and eventual C-O formations and C-C cleavages involving reactions with molecular oxygen. Acetate decomposition paths with non-oxidative kinetically-relevant steps exhibit similar free energy barriers to oxidative paths. In contrast, the non-oxidative ethylene path involving an ethylidyne intermediate exhibits a much lower barrier than paths with oxidative kinetically-relevant steps. Ethylene decomposition is very facile at low coverages but is more coverage-sensitive, leading to similar decomposition and VA formation barriers at coverages accessible at steady state, which is consistent with moderate VA selectivity in measurements and ethylene vs. acetate decomposition contributions assessed from regressed kinetic parameters. These insights provide a detailed framework for describing VA synthesis rates and selectivity on metallic catalyst surfaces.

Atomistic Insights into Solid-State Phase Transition Mechanisms of P2-Type Layered Mn Oxides for High-Energy Na-Ion Battery Cathodes

Mn-based layered oxides hold great promise as high-energy, cost-effective cathodes for sodium-ion batteries (NIBs), but repetitive Na+ cycling induces harmful phase transitions. Understanding these mechanisms is essential for designing better performing NIB cathodes. Applying density functional theory (DFT) and variable cell-nudged elastic band (VC-NEB) calculations, we provide atomistic insights into phase transformation pathways and energy barriers in P2-Na x MnO2 material and its Ni-doped variant. We reveal the key P2-to-OP4/O2 and P2-to-P2' transitions that occur across various sodiation levels, involving substantial rearrangements around the transition metal sites, with tetrahedral transition states accountable for energy barriers. Our analysis of bond length and angle distortions highlights that shear deformations are pivotal in triggering P-to-O gliding at low sodium levels. Based on these insights, our structural distortion metrics offer a straightforward and computationally efficient descriptor to evaluate structural integrity for these layered oxides, enabling the design of NIBs with improved stability and extended lifespan.

Revealing the nature of the second branch point in the catalytic mechanism of the Fe(ii)/2OG-dependent ethylene forming enzyme

Ethylene-forming enzyme (EFE) has economic importance due to its ability to catalyze the formation of ethylene and 3-hydroxypropionate (3HP). Understanding the catalytic mechanism of EFE is essential for optimizing the biological production of these important industrial chemicals. In this study, we implemented molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) to elucidate the pathways leading to ethylene and 3HP formation. Our results suggest that ethylene formation occurs from the propion-3-yl radical intermediate rather than the (2-carboxyethyl)carbonato-Fe(ii) (EFIV) intermediate, which conclusively acts as a precursor for 3HP formation. The results also explain the role of the hydrophobic environment surrounding the 2OG binding site in stabilizing the propion-3-yl radical, which defines their conversion to either ethylene or 3HP. Our simulations on the A198L EFE variant, which produces more 3HP than wild-type (WT) EFE based on experimental observations, predict that the formation of the EFIV intermediate was more favored than WT. Also, MD simulations on the EFIV intermediate in both WT and A198L EFE predicted that the water molecules approach the Fe center, which suggests the role of water molecules in the breakdown of the EFIV intermediate. QM/MM simulations on the EFIV intermediate of WT and A198L EFE predicted that the Fe-bound water molecule could provide a proton for the 3HP formation from EFIV. The study underscores the critical influence of the enzyme's hydrophobic environment and second coordination sphere residues in determining product distribution between ethylene and 3HP. These mechanistic insights lay a foundation for targeted enzyme engineering, aiming to improve the selectivity and catalytic efficiency of EFE in biological ethylene and 3HP production.

Surface-Dependent Role of Oxygen Vacancies in Dimethyl Carbonate Synthesis from CO2 and Methanol over CeO2 Catalysts

The conversion of CO2 into value-added commodity chemicals, such as dimethyl carbonate (DMC), represents an environmentally friendly approach to CO2 utilization. This study exhaustively investigates the influence of oxygen vacancies (Ov) on CeO2 catalysts and, in particular, the role of surface structure. By integrating density functional theory calculations with experimental synthesis, we analyze the complex reaction mechanisms involved in DMC synthesis over both oxidized (Sto-(111), Sto-(110), and Sto-(100)) and nonoxidized (Ovsub-(111), Ovsur-(110), and Ovsur-(100)) CeO2 catalysts. Our findings indicate that Ov on the (111) surface inhibits DMC formation, whereas Ov on the (110) and (100) surfaces promotes it. This differential behavior is primarily attributed to Ov's modulation of the microscopic coordination environment on distinct surfaces, which impacts the rate-limiting step of C-O bond formation: CO2 + OCH3 → CH3OCOO (monodentate methyl carbonate, MMC) and CH3OCO + OCH3 → DMC. Additionally, analysis of the highly active Sto-(111) and Ovsur-(110) catalysts shows that their unique surface coordination microenvironments mitigate steric hindrance and facilitate an optimal arrangement of Lewis acid sites in proximity to Lewis base sites, thereby enhancing the DMC activity. This work underscores the pivotal role of surface structure in determining the effects of Ov, paving the way for the rational design of CeO2-based catalysts for the direct synthesis of DMC from CO2 and methanol.

Decoding Solubility Signatures from Amyloid Monomer Energy Landscapes

This study investigates the energy landscapes of amyloid monomers, which are crucial for understanding protein misfolding mechanisms in Alzheimer's disease. While proteins possess inherent thermodynamic stability, environmental factors can induce deviations from native folding pathways, leading to misfolding and aggregation, phenomena closely linked to solubility. Using the UNOPTIM program, which integrates the UNRES potential into the Cambridge energy landscape framework, we conducted single-ended transition state searches and employed discrete path sampling to compute kinetic transition networks starting from PDB structures. These kinetic transition networks consist of local energy minima and the transition states that connect them, which quantify the energy landscapes of the amyloid monomers. We defined clusters within each landscape using energy thresholds and selected their lowest-energy structures for the structural analysis. Applying graph convolutional networks, we identified solubility trends and correlated them with structural features. Our findings identify specific minima with low solubility, characteristic of aggregation-prone states, highlighting the key residues that drive reduced solubility. Notably, the exposure of the hydrophobic residue Phe19 to the solvent triggers a structural collapse by disrupting the neighboring helix. Additionally, we investigated selected minima to determine the first passage times between states, thereby elucidating the kinetics of these energy landscapes. This comprehensive approach provides valuable insights into the thermodynamics and kinetics of Aβ monomers. By integration of multiple analytical techniques to explore the energy landscapes, our study investigates structural features associated with reduced solubility. These insights have the potential to inform future therapeutic strategies aimed at addressing protein misfolding and aggregation in neurodegenerative diseases.

Facilitating Transition State Search with Minimal Conformational Sampling Using Reaction Graph

Elucidating transition states (TSs) is crucial for understanding chemical reactions. The reliability of traditional TS search approaches depends on input conformations that require significant effort to prepare. Previous automated methods for generating input reaction conformations typically involve extensive exploration of a large conformational space. Such exhaustive search can be complicated by the rapid growth of the conformational space, especially for reactions involving many rotatable bonds, multiple reacting molecules, and numerous bond formations and dissociations. To address this problem, we propose a new approach that generates reaction conformations for TS searches with minimal reliance on sampling. This method constructs a pseudo-TS structure based on a reaction graph containing bond formation and dissociation information and modifies it to produce reactant and product conformations. Tested on three different benchmarks, our method consistently generated suitable conformations without necessitating extensive sampling, demonstrating its potential to significantly improve the applicability of automated TS searches. This approach offers a valuable tool for a broad range of applications such as reaction mechanism analysis and network exploration.

Transition State Searching Accelerated by Neural Network Potential

Understanding transition states is pivotal in the design of efficient chemical processes and catalysts. However, identifying transition states is challenging due to the resource-intensive and iterative nature of current computational methods. This study integrates neural network potentials with physical models to enhance the transition state prediction. Different neural network potentials and transition states locating algorithms are benchmarked. By combining NequIP with the energy-weighted Climbing Image-Nudged Elastic Band (EW-CI-NEB) method, we achieved highly accurate transition state predictions, significantly surpassing semiempirical methods in accuracy and greatly outpacing density functional theory in efficiency. Additionally, the transferability of the model was evaluated using a NequIP model trained on a refined subset of the dataset, and the model's performance was further improved through active learning. This method can directly search for transition states in given reactions or serve as an efficient tool for generating initial guesses of transition state structures, significantly reducing manual effort.

Automatic Process Exploration through Machine Learning Assisted Transition State Searches

We present an efficient automatic process explorer (APE) framework to overcome the reliance on human intuition to empirically establish relevant elementary processes of a given system, e.g., in prevalent kinetic Monte Carlo (kMC) simulations based on fixed process lists. Use of a fuzzy machine learning classification algorithm minimizes redundancy in the transition-state searches by driving them toward hitherto unexplored local atomic environments. APE application to island diffusion at a Pd(100) surface immediately reveals a large number of, up to now, disregarded low-barrier collective processes that lead to a significant increase in the kMC-determined island diffusivity as compared to classic surface hopping and exchange diffusion mechanisms.

Scaling relations of CO2 hydrogenation and dissociation on single metal atom doped In2O3 catalysts with promoted oxygen vacancy sites

In this work, we conducted a computational study on single atom doped In2O3 catalysts with 12 transition metals (Fe-Cu, Ru-Ag, Os-Au) through density functional theory (DFT) calculations, by investigating the dissociation of H2, and the dissociation and hydrogenation of CO2. From the thermodynamic-kinetic scaling relationships such as Brønsted-Evans-Polanyi (BEP) and transition-state scaling (TSS) relations, we establish the descriptors for the energy barriers and improve our understanding of the synergistic catalytic effect of oxygen vacancies and single atoms. We find that the adsorption energy of the H adatom on the perfect surface can serve as an effective descriptor for the dissociation energy barrier of H2 on this surface, and the formation energy of the oxygen vacancy can serve as an effective descriptor for the energy barrier of CO2 hydrogenation to HCOO as well as the energy barrier of CO2 direct dissociation.

Anomalous Role of Carbon in Pd-Catalyzed Selective Hydrogenation

Carbonaceous species, including subsurface carbidic carbon and surface carbon, play crucial roles in heterogeneous catalysis. Many reports suggested the importance of subsurface carbon in the selective hydrogenation of alkynes over Pd-based catalysts. However, the role of surface carbon has been largely overlooked. We demonstrate that subsurface carbon in Pd is not responsible for the selectivity in acetylene hydrogenation. In contrast, the structure of surface carbonaceous species plays a decisive role in hydrogenation selectivity. Electron microscopy and spectroscopy evidence, along with theoretical modelling, reveal that partial graphitization of surface carbonaceous species results in unique spatial confinement of surface reaction intermediates, thus altering the reaction energy landscape in favour of ethylene desorption as opposed to over-hydrogenation. This mechanism for selectivity control is analogous to enzyme catalysis, where the active centers selectively attract reactants and release products. Similar mechanism may be present in CO/CO2 hydrogenation and alkane dehydrogenation reactions.

Palladium Nanocubes with {100} Facets for Hydrogen Evolution Reaction: Synthesis, Experiment and Theory

Spatially separated palladium nanocubes (Pd NCs) terminated by {100} facets are synthesized using direct micelles approach. The stepwise seed-mediated growth of Pd NCs is applied for the first time. The resulting Pd NCs are thoroughly characterized by HR-TEM, XPS, Raman, ATR-FTIR, TGA, and STEM-EDX spectroscopies. Some traces of residual stabilizer (polyvinylpyrrolidone, PVP) attached to the vertices of Pd NCs are identified after the necessary separation-washing procedure, however, it is vital to avoid aggregation of the NCs. Pd NCs are subsequently and uniformly loaded on Vulcan carbon (≈20 wt.%) for the electrochemical hydrogen cycling. By post-mortem characterizations, it is revealed that their shape and size remained very stable after all electrochemical experiments. However, a strong effect of the NCs size on their hydrogen interaction is revealed. Hydrogen absorption capacity, measured as the H:Pd ratio, ranges from 0.28 to 0.48, while hydrogen evolution and oxidation reactions (HER and HOR) kinetics decrease from 15.5 to 4.6 mA.mg Pd -1 between ≈15 and 34 nm of Pd NCs, respectively. Theoretical calculations further reveal that adsorption of H atoms and their penetration into the Pd lattice tailors the NCs electronic structure, which in turn controls the kinetics of HER, experimentally observed by the electrochemical tests. This work may pave the way to the design of highly active electrocatalysts for efficient HER stable for a long reactive time. In particular, obtained results might be transferred to active Pd-alloy-based NCs terminated by {100} facets.

Multiple Reaction Pathways for Oxygen Evolution as a Key Factor for the Catalytic Activity of Nickel-Iron (Oxy)Hydroxides

We present a comprehensive theoretical study, using state-of-the-art density functional theory simulations, of the structural and electrochemical properties of amorphous pristine and iron-doped nickel-(oxy)hydroxide catalyst films for water oxidation in alkaline solutions, referred to as NiCat and Fe:NiCat. Our simulations accurately capture the structural changes in locally ordered units, as reported by X-ray absorption spectroscopy, when the catalyst films are activated by exposure to a positive potential. We emphasize the critical role of proton-coupled electron transfer in the reversible oxidation of Ni(II) to Ni(III/IV) during this activation. After establishing the structural models of NiCat and Fe:NiCat consistent with experimental data, we used them to explore the atomistic mechanism of the oxygen evolution reaction (OER), which is triggered once the applied potential exceeds the overpotential required for water oxidation and oxygen production. We quantitatively compared seven OER pathways applicable to both the adsorbate evolution mechanism (AEM) and the lattice-oxygen-mediated mechanism (LOM) families, elucidating how iron significantly enhances the catalytic activity of Fe:NiCat compared to NiCat. Our findings suggest that simple metal-oxygen-metal motifs, common on the surface of both crystalline and amorphous metal (oxy)hydroxide films, can promote both AEM and LOM mechanisms under typical OER conditions. Furthermore, we propose that the elusive role of iron lies in the distinct behavior of Ni(IV)-O and Fe(IV)-O bonds in key intermediates preceding the formation of the O-O bond, with Fe ions lowering the potential needed to form these intermediates across the investigated pathways.

Microscopic Insights into the Catalytic Activity-Stability Trade-Off on Copper Nanoclusters for CO2 Hydrogenation to HCOOH

Lowly coordinated copper clusters are the most cost-effective benchmark catalysts for CO2 hydrogenation, but there is a meticulous balance between catalytic activity and stability. Herein, density functional theory (DFT) calculations are implemented to examine the catalytic performance of Cun nanoclusters (n = 4, 8, 16, 32) in CO2-to-HCOOH conversion. Facile activation of H2 is observed with significant electron transfer from Cun to antibonding orbitals of H2; conversely, the C-O bond of CO2 is poorly activated due to a low degree of orbital overlap. During the reaction, structural fluxionality occurs on Cu4 and Cu8 because of the low stability; however, negligible deformation is observed on Cu16 and Cu32. In addition, Cu16 achieves a good balance between the kinetics of each elementary reaction, which is, however, difficult to be maintained on Cu4, Cu8, and Cu32. Therefore, Cu16 satisfies the trade-off between activity and stability in CO2-to-HCOOH conversion. Energy decomposition analysis clarifies that the activation barrier of the second hydrogenation originates from the energy of hydride desorption, the electronic repulsion energy due to hydroxyl group formation, as well as the energy for local Cu-O bond cleavage. The high energy demand on the second hydrogenation is mainly sourced from the last term. From the bottom up, this work provides microscopic insights into the catalytic activity-stability trade-off in CO2 hydrogenation to HCOOH and would facilitate the rational design of advanced catalysts for the high-value utilization of CO2 exhaust gas.

Energy Landscapes for the Unitary Coupled Cluster Ansatz

The unitary coupled cluster (UCC) approach has been one of the most popular wavefunction parametrizations for the variational quantum eigensolver due to the relative ease of optimization compared to hardware-efficient ansätze. In this contribution, we explore the energy landscape of the unitary coupled cluster singles and doubles (UCCSD) wavefunction for two commonly employed benchmark systems, lithium hydride and the nitrogen dimer. We investigate the organization of the solution space in terms of local minima and show how it changes as the number and order of operators of the UCC ansatz are varied. Surprisingly, we find that in all cases, the UCCSD energy has numerous low-lying minima connected by high energy transition states. Additionally, the energy spread of the minima that lie in the same band as the global minimum may exceed chemical accuracy, making the location of the true global minimum especially challenging.

Proton Diffusion in Orthorhombic Perovskite Sulfides

The proton mobility in perovskite sulfides is investigated. Both stable as well as unstable compounds are considered to cover a wide range of ABS3 compounds, the latter were selected based on a preferably small energy difference to the thermodynamic phase equilibrium. Density functional theory (DFT) is used to analyze all possible metastable hydrogen positions within the (001) and (110)/(11̅0) planes spanned by the sulfur atoms. The nudged elastic band (NEB) method is used to determine the activation energy barriers between neighboring hydrogen sites. From the hydrogen positions and the activation energies, the diffusion rate is calculated with an approach based on the Markovian master equation. Proton mobility is analyzed in detail for a subset of compounds, while a simplified analysis of the zigzag-paths in the prominent [001] and [010] directions is used to explore a wider chemical space. Room temperature diffusion coefficients of the order of 10-6 cm2/s are predicted to be feasible in Zr-based compounds. The A- and B-site occupants influence mobility mainly due to their impact on crystallography, because symmetry-breaking distortions that reduce the S-S distance have a leading influence on reducing activation energies, but they also induce significant anisotropy.

Optimizing selectivity via steering dominant reaction mechanisms in steam reforming of methanol for hydrogen production

Enhancing selectivity towards specific products remains a pivotal challenge in energy catalysis. Herein, we present a strategy to refine selectivity via pathway optimization, exemplified by the rational design of catalysts for methanol steam reforming. Over traditional Pd/ZnO catalysts, the direct decomposition of key intermediates CH2O* into CO and H2 on PdZn alloys competes with the oxidation of CH2O* to CO2, leading to inferior selectivity in product distribution. To address this challenge, Cu is introduced to modify the catalytic dynamics, lowering the dissociation energy barrier of water to provide more active hydroxyl groups for the oxidation of CH2O*. Simultaneously, the CO desorption energy barrier on PdCu alloys is elevated, thereby hindering CH2O* decomposition. This dual functionality enhances both the selectivity and activity of the methanol steam reforming reaction. By modulating the activation patterns of key intermediate species, this approach provides new insights into catalyst design for improved reaction selectivity.

Tandem reductive amination and deuteration over a phosphorus-modified iron center

Deuterated amines are key building blocks for drug synthesis and the identification of metabolites of new pharmaceuticals, which drives the search for general, efficient, and widely applicable methods for the selective synthesis of such compounds. Here, we describe a multifunctional phosphorus-doped carbon-supported Fe catalyst with highly dispersed isolated metal sites that allow for tandem reductive amination-deuteration sequences. The optimal phosphorus-modified Fe-based catalyst shows excellent performance in terms of both reactivity and regioselectivity for a wide range of deuterated anilines, amines, bioactive complexes, and drugs (>50 examples). Experiments on the gram scale and on catalyst recycling show the application potential of this method. Beyond the direct applicability of the developed method, the described approach opens a perspective for the development of multifunctional single-atom catalysts in other value-adding organic syntheses.

First-Principles Kinetic Monte Carlo Simulations for Single-Cluster Catalysis: Study of CO2 and CH4 Conversion on Pt/HfC

The deposition of small transition metal (TM) clusters on transition metal carbides (TMC) gives rise to bifunctional catalysts with multiple active sites. This family of single-cluster catalysts (SCCs) offers exciting opportunities for enabling a wider range of chemical reactions owing to their strong metal-support interactions, which drastically modify the catalytic properties of the supported metal atoms. In this work, we use first-principles Kinetic Monte Carlo (KMC) simulations to investigate the conversion of CO2 and CH4 on Pt/HfC, which was identified as the most promising TM/TMC combination in a previous DFT-based high-throughput screening study. We analyze the interplay between the Pt clusters and the HfC support, evaluating the catalytic activity, selectivity, and adlayer composition across a broad range of operating conditions (p A , p B , and T) and Pt loadings. This study evaluates five different industrial processes, including the dry reforming (DRM), steam reforming (SRM), and partial oxidation (POM) of methane, as well as the water-gas shift (WGS) reaction and its reverse (RWGS). Our results demonstrate that the deposition of Pt clusters on HfC systematically enhances the catalytic performance, even at a Pt loading as low as ∼0.02 ML. This work illustrates the extensive catalytic benefits of SCCs and highlights the importance of considering diffusion steps and lateral interactions in kinetic modeling.

Mechanism and catalytic activity of the water-gas shift reaction on a single-atom alloy Al1/Cu (111) surface

The mechanism and activity of the water-gas shift reaction (WGSR) on single-atom alloy Al1/Cu (111) and Cu (111) surfaces were studied using GGA-PBE-D3. Al1/Cu (111) exhibited bifunctional active sites, with the Al site being positively charged and the Cu site negatively charged due to electronic interactions. This led to selective adsorption of H2O and CO. Al1/Cu (111) promoted H2O adsorption and dissociation, reducing the energy barrier to 0.67 eV compared with 1.13 eV on the Cu (111) surface. Meanwhile, Cu served as the active site for H2 formation, which is the rate-determining step, with an energy barrier of 0.95 eV. The Al-O and Cu-C bonds cooperatively increased the interaction strength of O-containing intermediates. Al1/Cu (111) promoted the whole WGSR through cooperativity, reducing the overall apparent activation energy. This work gives insights for the design of single atom alloy (SAA) catalysts with p-p orbital energy level matching, which facilitates orbital interactions between Al and H2O, thus achieving excellent WGSR activity.

Effect of Exact Exchange on the Energy Landscape in Self-Consistent Field Theory

Density functional approximations can reduce the spin symmetry breaking observed for self-consistent field (SCF) solutions compared to Hartree-Fock theory, but the amount of exact Hartree-Fock (HF) exchange appears to be a key determinant in broken Ŝ2 symmetry. To elucidate the precise role of exact exchange, we investigate the energy landscape of unrestricted Hartree-Fock and Kohn-Sham density functional theory for benzene and square cyclobutadiene, which provide paradigmatic examples of closed-shell and open-shell electronic structures, respectively. We find that increasing the amount of exact exchange leads to more local SCF minima, which can be characterized as combinatorial arrangements of unpaired electrons in the carbon π system. Furthermore, we studied the pathways connecting local minima to understand the relationships between different solutions. Our analysis reveals a subtle balance between one- and two-body interactions in determining SCF symmetry breaking, shedding new light on the physical driving forces for spin-symmetry-broken solutions in SCF approaches.

Generative Model for Constructing Reaction Path from Initial to Final States

Mapping the chemical reaction pathways and their corresponding activation barriers is a significant challenge in molecular simulation. Given the inherent complexities of 3D atomic geometries, even generating an initial guess of these paths can be difficult for humans. This paper presents an innovative approach that utilizes neural networks to generate initial guesses for reaction pathways based on the initial state and learning from a database of low-energy transition paths. The proposed method is initiated by inputting the coordinates of the initial state, followed by progressive alterations to its structure. This iterative process culminates in the generation of the guess reaction path and the coordinates of the final state. The method does not require one-the-fly computation of the actual potential energy surface and is therefore fast-acting. The application of this geometry-based method extends to complex reaction pathways illustrated by organic reactions. Training was executed on the Transition1x data set of organic reaction pathways. The results revealed the generation of reactions that bore substantial similarities with the test set of chemical reaction paths. The method's flexibility allows for reactions to be generated either to conform to predetermined conditions or in a randomized manner.

Mechanistical Study on Substrate-Controlled Highly Selective [2+2] and [2+3] Cycloaddition Reactions

Polycyclic conjugated hydrocarbons have acquired increased interests recently because of their potential applications in electronic devices. On metal surfaces, the selective synthesis of four- and five-membered carbon rings remains challenging due to the presence of diverse reaction pathways. Here, utilizing the same precursor molecule, we successfully achieved substrate-controlled highly selective cycloaddition reactions towards four- and five-membered carbon rings. A 97 % yield for four-membered carbon rings on Au(111), while a 96 % yield towards five-membered carbon rings is achieved on Ag(111). The detailed topological structures of the reaction products are carefully examined by bond-resolving scanning tunneling microscopy (BR-STM) imaging with a CO functionalized tip. The underlying mechanism of the novel surface-directed reaction selectivity is elucidated by extensive density functional theory (DFT) calculations. Our study paves the way for high selective synthesis of polycyclic conjugated hydrocarbons with non-benzenoid rings.

Lattice-Doped Ir Cooperating with Surface-Anchored IrOx for Acidic Oxygen Evolution Reaction with Ultralow Ir Loading

Reducing iridium (Ir) loading while maintaining efficiency and stability is crucial for the acidic oxygen evolution reaction (OER). In this study, we develop a synthetic method of sequential electrochemical deposition and high-temperature thermal shock to produce an IrOx/Ir-WO3 electrocatalyst with ∼1.75 nm IrOx nanoparticles anchoring on Ir-doped WO3 nanosheets. The IrOx/Ir-WO3 electrocatalyst with a low Ir loading of 0.035 mg cm-2 demonstrates a low overpotential of 239 mV to achieve a current density of 10 mA cm-2 and a mass activity of 6.6 × 104 A gIr-1 @1.75 V vs RHE in 0.5 M H2SO4. IrOx/Ir-WO3 on carbon paper as the anode and Pt/C as the cathode work stably for 40 h at 30 mA cm-2 in a proton exchange membrane water electrolyzer. It is found that the cooperation of lattice-doped Ir and surface-anchored IrOx enhances the activity and stability of IrOx/Ir-WO3 for acidic OER. Specifically, the doped Ir reduces the electron density of the anchored IrOx, thus optimizing the adsorption energy of oxygen-containing intermediates and the kinetic barrier of H2O dissociation, leading to an enhanced activity of IrOx/Ir-WO3. Also, the Ir-WO3 support provides electrons to retard the overoxidation and dissolution of Ir atoms from the anchored IrOx during acidic OER.

Theoretical screening of single-atom catalysts (SACs) on Mo2TiC2O2 MXene for methane activation

Producing value-added chemicals and fuels from methane (CH4) under mild conditions efficiently utilizes this cheap and abundant feedstock, promoting economic growth, energy security, and environmental sustainability. However, the first CH bond activation is a significant challenge and requires high energy. Efficient catalysts have been sought for utilizing CH4 at low temperatures including emerging single-atom catalysts (SACs). In this work, we screened fourteen transition metals (TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ru, Rh, Pd, Pt) doped at a single oxygen vacancy in Mo2TiC2O2 (TMSA-Mo2TiC2O2 SACs) for methane activation using density functional theory (DFT) calculations. Our results reveal that methane adsorption is thermodynamically stable on all simulated TMSA-Mo2TiC2O2 SACs, with the adsorption energies (Eads) ranging from -0.92 to -0.40 eV. For the CH activation process, Ru-SAC exhibits the lowest activation barrier (Ea) of 0.22 eV. In summary, Ru-, Rh-, Co-, V-, Cr-, Ti-, and Pt-SACs demonstrate promising catalytic properties for methane activation, with Ea values below 1.0 eV and an exothermic nature. Our findings pave the way for the design and development of novel single-atom catalysts in MXene materials, applicable not only for methane activation but also for other alkane dehydrogenation processes.

Atomically intimate assembly of dual metal-oxide interfaces for tandem conversion of syngas to ethanol

Selective conversion of syngas to value-added higher alcohols (containing two or more carbon atoms), particularly to a specific alcohol, is of great interest but remains challenging. Here we show that atomically intimate assembly of FeOx-Rh-ZrO2 dual interfaces by selectively architecting highly dispersed FeOx on ultrafine raft-like Rh clusters supported on tetragonal zirconia enables highly efficient tandem conversion of syngas to ethanol. The ethanol selectivity in oxygenates reached ~90% at CO conversion up to 51%, along with a markedly high space-time yield of ethanol of 668.2 mg gcat-1 h-1. In situ spectroscopic characterization and theoretical calculations reveal that Rh-ZrO2 interface promotes dissociative CO activation into CHx through a formate pathway, while the adjacent Rh-FeOx interface accelerates subsequent C-C coupling via nondissociative CO insertion. Consequently, these dual interfaces in atomic-scale proximity with complementary functionalities synergistically boost the exclusive formation of ethanol with exceptional productivity in a tandem manner.

Computational Insights Into Corrosion Inhibition Mechanism: Dissociation of Imidazole on Iron Surface

Corrosion inhibitors are widely used to mitigate safety risks and economic losses in engineering, yet post-adsorption processes remain underexplored. In this study, we employed density functional theory calculations with a periodic model to investigate the dissociation mechanisms of imidazole on the Fe(100) surface. Imidazole was found to adsorb optimally in a parallel orientation, with an adsorption energy of -0.88 eV. We explored two dissociation pathways: CH and NH bond cleavages and found CH dissociation having a lower activation barrier of 0.46 eV. Intriguingly, an alternative indirect route CH dissociation pathway involving a tilted intermediate state was found to be competitive. Both indirect and direct CH dissociation pathways are energetically more favorable than NH cleavage. Molecular dynamics simulations reveal that indirect CH dissociation occurs rapidly. This study proposes an alternative protective mechanism involving dissociated imidazole inhibitors, offering new insights for corrosion inhibitor design.