Catalysis. Assessing the reliability of calculated catalytic ammonia synthesis rates

Ammonia decomposition mediated at nitrogen vacancies on NaCl-type binary metal nitrides supporting transition metal nanoparticles

The ability of NaCl-type binary transition metal nitrides (incorporating La, Ce, Y, Zr or Hf) to act as catalytic supports facilitating ammonia decomposition was examined. The effect of nitrogen vacancies formed on nitrides can be understood in terms of the ionic radii of the metal cations. A clear correlation between the N2 desorption temperature and catalytic activity was found.

What Metals Should Be Used to Mediate Electrosynthesis of Ammonia from Nitrogen and Hydrogen from a Thermodynamic Standpoint?

Recently, metal-mediated electrochemical conversion of nitrogen and hydrogen to ammonia (M-eNRRs) has been attracting intense research attention as a potential route for ammonia synthesis under ambient conditions. However, which metals should be used to mediate M-eNRRs remains unanswered. This work provides an extensive comparison of the energy consumption in the classical Haber Bosch (H-B) process and the M-eNRRs. The results indicate that when employing lithium and calcium, metals popularly used to mediate the M-eNRRs, the energy consumption is more than 10 times greater than that of the H-B process even assuming a 100% Faradaic efficiency and zero overpotentials. Only electrosynthesis with a cell voltage not exceeding 0.38 V might have the potential to rival the H-B process from an energetic perspective. A further analysis of other metals in the periodic table reveals that only some heavy metals, including In, Tl, Co, Ni, Ga, Mo, Sn, Pb, Fe, W, Ge, Re, Bi, Cu, Po, Tc, Ru, Rh, Ag, Hg, Pd, Ir, Pt, and Au, can potentially consume less energy than that of the H-B process purely from a thermodynamic standpoint, but whether they can activate N2 under ambient conditions is yet to be explored. This work shows the importance of performing thermodynamic analysis for the development of an innovative strategy to synthesize ammonia with the ultimate goal of replacing the H-B process on a large scale.

Reversible Hydrogen Electrode (RHE) Scale Dependent Surface Pourbaix Diagram at Different pH

In the analysis of electrocatalysis mechanisms and the design of catalysts, the effect of electrochemistry-induced surface coverage is a critical consideration that should not be overlooked. The surface Pourbaix diagram emerges as a fundamental tool in this context, providing essential insights into the surface coverage of adsorbates generated via electrochemical potential-driven water activation. A classic surface Pourbaix diagram considers the pH effects by correcting the free energy of H+ ions by the concentration-dependent term: -kBT ln(10) × pH, which is independent of the reversible hydrogen electrode (RHE) scale. However, this is sometimes inconsistent with the experimentally observed potential-dependent surface coverage at an RHE scale, especially under high-pH conditions. Here, we derived the pH-dependent surface Pourbaix diagram at an RHE scale by considering the energetics computed by density functional theory with the Bayesian Error Estimation Functional with van der Waals corrections (BEEF-vdW), the electric field effects, the derived adsorption-induced dipole moment and polarizability, and the potential of zero-charge. Using Pt(111) as the typical example, we found that the surface coverage predicted by the proposed RHE-dependent surface Pourbaix diagram can significantly minimize the discrepancy between theory and experimental observations, especially under neutral-alkaline, moderate-potential conditions. This work provides a new methodology and establishes guidelines for the precise analysis of the surface coverage prior to the evaluation of the activity of an electrocatalyst.

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

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

GPAW: An open Python package for electronic structure calculations

We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using three different wave-function representations, namely real-space grids, plane waves, and numerical atomic orbitals. The three representations are complementary and mutually independent and can be connected by transformations via the real-space grid. This multi-basis feature renders GPAW highly versatile and unique among similar codes. By virtue of its modular structure, the GPAW code constitutes an ideal platform for the implementation of new features and methodologies. Moreover, it is well integrated with the Atomic Simulation Environment (ASE), providing a flexible and dynamic user interface. In addition to ground-state DFT calculations, GPAW supports many-body GW band structures, optical excitations from the Bethe-Salpeter Equation, variational calculations of excited states in molecules and solids via direct optimization, and real-time propagation of the Kohn-Sham equations within time-dependent DFT. A range of more advanced methods to describe magnetic excitations and non-collinear magnetism in solids are also now available. In addition, GPAW can calculate non-linear optical tensors of solids, charged crystal point defects, and much more. Recently, support for graphics processing unit (GPU) acceleration has been achieved with minor modifications to the GPAW code thanks to the CuPy library. We end the review with an outlook, describing some future plans for GPAW.

Highly loaded bimetallic iron-cobalt catalysts for hydrogen release from ammonia

Ammonia is a storage molecule for hydrogen, which can be released by catalytic decomposition. Inexpensive iron catalysts suffer from a low activity due to a too strong iron-nitrogen binding energy compared to more active metals such as ruthenium. Here, we show that this limitation can be overcome by combining iron with cobalt resulting in a Fe-Co bimetallic catalyst. Theoretical calculations confirm a lower metal-nitrogen binding energy for the bimetallic catalyst resulting in higher activity. Operando spectroscopy reveals that the role of cobalt in the bimetallic catalyst is to suppress the bulk-nitridation of iron and to stabilize this active state. Such catalysts are obtained from Mg(Fe,Co)2O4 spinel pre-catalysts with variable Fe:Co ratios by facile co-precipitation, calcination and reduction. The resulting Fe-Co/MgO catalysts, characterized by an extraordinary high metal loading reaching 74 wt.%, combine the advantages of a ruthenium-like electronic structure with a bulk catalyst-like microstructure typical for base metal catalysts.

Invariant Molecular Representations for Heterogeneous Catalysis

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

Operando probing of the surface chemistry during the Haber-Bosch process

The large-scale conversion of N2 and H2 into NH3 (refs. 1,2) over Fe and Ru catalysts3 for fertilizer production occurs through the Haber-Bosch process, which has been considered the most important scientific invention of the twentieth century4. The active component of the catalyst enabling the conversion was variously considered to be the oxide5, nitride2, metallic phase or surface nitride6, and the rate-limiting step has been associated with N2 dissociation7-9, reaction of the adsorbed nitrogen10 and also NH3 desorption11. This range of views reflects that the Haber-Bosch process operates at high temperatures and pressures, whereas surface-sensitive techniques that might differentiate between different mechanistic proposals require vacuum conditions. Mechanistic studies have accordingly long been limited to theoretical calculations12. Here we use X-ray photoelectron spectroscopy-capable of revealing the chemical state of catalytic surfaces and recently adapted to operando investigations13 of methanol14 and Fischer-Tropsch synthesis15-to determine the surface composition of Fe and Ru catalysts during NH3 production at pressures up to 1 bar and temperatures as high as 723 K. We find that, although flat and stepped Fe surfaces and Ru single-crystal surfaces all remain metallic, the latter are almost adsorbate free, whereas Fe catalysts retain a small amount of adsorbed N and develop at lower temperatures high amine (NHx) coverages on the stepped surfaces. These observations indicate that the rate-limiting step on Ru is always N2 dissociation. On Fe catalysts, by contrast and as predicted by theory16, hydrogenation of adsorbed N atoms is less efficient to the extent that the rate-limiting step switches following temperature lowering from N2 dissociation to the hydrogenation of surface species.

MultiXC-QM9: Large dataset of molecular and reaction energies from multi-level quantum chemical methods

Well curated extensive datasets have helped spur intense molecular machine learning (ML) method development activities over the last few years, encouraging nonchemists to be part of the effort as well. QM9 dataset is one of the benchmark databases for small molecules with molecular energies based on B3LYP functional. G4MP2 based energies of these molecules were published later. To enable a wide variety of ML tasks like transfer learning, delta learning, multitask learning, etc. with QM9 molecules, in this article, we introduce a new dataset with QM9 molecule energies estimated with 76 different DFT functionals and three different basis sets (228 energy numbers for each molecule). We additionally enumerated all possible A ↔ B monomolecular interconversions within the QM9 dataset and provided the reaction energies based on these 76 functionals, and basis sets. Lastly, we also provide the bond changes for all the 162 million reactions with the dataset to enable structure- and bond-based reaction energy prediction tools based on ML.

Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties

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

Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling

Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are rungs on a multiscale workflow. Establishing connections across these rungs and integration with experiments have been challenging. Here, operando catalyst structure prediction techniques using density functional theory simulations and ab initio thermodynamics calculations, molecular dynamics, and machine learning techniques are presented. Surface structure characterization by computational spectroscopic and machine learning techniques is then discussed. Hierarchical approaches in kinetic parameter estimation involving semi-empirical, data-driven, and first-principles calculations and detailed kinetic modeling via mean-field microkinetic modeling and kinetic Monte Carlo simulations are discussed along with methods and the need for uncertainty quantification. With these as the background, this article proposes a bottom-up hierarchical and closed loop modeling framework incorporating consistency checks and iterative refinements at each level and across levels.

Unraveling the reaction mechanisms for furfural electroreduction on copper

Electrochemical routes for the valorization of biomass-derived feedstock molecules offer sustainable pathways to produce chemicals and fuels. However, the underlying reaction mechanisms for their electrochemical conversion remain elusive. In particular, the exact role of proton-electron coupled transfer and electrocatalytic hydrogenation in the reaction mechanisms for biomass electroreduction are disputed. In this work, we study the reaction mechanism underlying the electroreduction of furfural, an important biomass-derived platform chemical, combining grand-canonical (constant-potential) density functional theory-based microkinetic simulations and pH dependent experiments on Cu under acidic conditions. Our simulations indicate the second PCET step in the reaction pathway to be the rate- and selectivity-determining step for the production of the two main products of furfural electroreduction on Cu, i.e., furfuryl alcohol and 2-methyl furan, at moderate overpotentials. We further identify the source of Cu's ability to produce both products with comparable activity in their nearly equal activation energies. Furthermore, our microkinetic simulations suggest that surface hydrogenation steps play a minor role in determining the overall activity of furfural electroreduction compared to PCET steps due to the low steady-state hydrogen coverage predicted under reaction conditions, the high activation barriers for surface hydrogenation and the observed pH dependence of the reaction. As a theoretical guideline, low pH (<1.5) and moderate potential (ca. -0.5 V vs. SHE) conditions are suggested for selective 2-MF production.

Tracking C-H bond activation for propane dehydrogenation over transition metal catalysts: work function shines

The activation of the C-H bond in heterogeneous catalysis plays a privileged role in converting light alkanes into commodity chemicals with a higher value. In contrast to traditional trial-and-error approaches, developing predictive descriptors via theoretical calculations can accelerate the process of catalyst design. Using density functional theory (DFT) calculations, this work describes tracking C-H bond activation of propane over transition metal catalysts, which is highly dependent on the electronic environment of catalytic sites. Furthermore, we reveal that the occupancy of the antibonding state for metal-adsorbate interaction is the key factor in determining the ability to activate the C-H bond. Among 10 frequently used electronic features, the work function (W) exhibits a strong negative correlation with C-H activation energies. We demonstrate that e^-W can effectively quantify the ability of C-H bond activation, surpassing the predictive capacity of the d-band center. The C-H activation temperatures of the synthesized catalysts also confirm the effectiveness of this descriptor. Apart from propane, e^-W applies to other reactants like methane.

Microenvironment Engineering to Promote Selective Ammonia Electrosynthesis from Nitrate over a PdCu Hollow Catalyst

The electrosynthesis of recyclable ammonia (NH₃ ) from nitrate under ambient conditions is of great importance but still full of challenges for practical application. Herein, an efficient catalyst design strategy is developed that can engineer the surface microenvironment of a PdCu hollow (PdCu-H) catalyst to confine the intermediates and thus promote selective NH₃ electrosynthesis from nitrate. The hollow nanoparticles are synthesized by in situ reduction and nucleation of PdCu nanocrystals along a self-assembled micelle of a well-designed surfactant. The PdCu-H catalyst shows a structure-dependent selectivity toward the NH₃ product during the nitrate reduction reaction (NO₃ ^- RR) electrocatalysis, enabling a high NH₃ Faradaic efficiency of 87.3% and a remarkable NH₃ yield rate of 0.551 mmol h^-1 mg^-1 at -0.30 V (vs reversible hydrogen electrode). Moreover, this PdCu-H catalyst delivers high electrochemical performance in the rechargeable zinc-NO₃ ^- battery. These results provide a promising design strategy to tune catalytic selectivity for efficient electrosynthesis of renewable NH₃ and feedstocks.

Mesoporous PdN Alloy Nanocubes for Efficient Electrochemical Nitrate Reduction to Ammonia

Developing highly active and selective electrocatalysts for electrochemical nitrate reduction reaction (NITRR) is very important for synthesizing recyclable ammonia (NH₃ ) in an economic and environmentally friendly manner. Despite some encouraging progress, their activity and selectivity have been remarkably slower than expected. In this manuscript, mesoporous palladium-nonmetal (meso-PdX) nanocubes (NCs) are reported as a new series of highly efficient electrocatalysts for selective nitrate reduction reaction (NITRR) electrocatalysis to NH₃ . The samples feature uniformly alloyed compositions and highly penetrated mesopores with abundant highly active sites and optimized electronic structures. The best meso-PdN NCs hold an outstanding NITRR activity and selectivity with a remarkable NH₃ Faradaic efficiency of 96.1% and a yield rate of 3760 µg h^-1 mg^-1 , suppressing the state-of-the-art electrocatalysts. Meanwhile, meso-PdN NCs are electrocatalytically stable, retaining well the activity and selectivity of NO₃ ^- -to-NH₃ electrocatalysis for more than 20 cycles. Detailed mechanism studies ascribe the superior performance to combined compositional and structural synergies of meso-PdN NCs that not only promote the adsorption (reactivity) of NO₃ ^- and the desorption of NH₃ but also increase the retention time of key intermediates for the deeper NITRR electrocatalysis to NH₃ through an eight-electron pathway.

SBH17: Benchmark Database of Barrier Heights for Dissociative Chemisorption on Transition Metal Surfaces

Accurate barriers for rate controlling elementary reactions on metal surfaces are key to understanding, controlling, and predicting the rate of heterogeneously catalyzed processes. While barrier heights for gas phase reactions have been extensively benchmarked, dissociative chemisorption barriers for the reactions of molecules on metal surfaces have received much less attention. The first database called SBH10 and containing 10 entries was recently constructed based on the specific reaction parameter approach to density functional theory (SRP-DFT) and experimental results. We have now constructed a new and improved database (SBH17) containing 17 entries based on SRP-DFT and experiments. For this new SBH17 benchmark study, we have tested three algorithms (high, medium, and light) for calculating barrier heights for dissociative chemisorption on metals, which we have named for the amount of computational effort involved in their use. We test the performance of 14 density functionals at the GGA, GGA+vdW-DF, and meta-GGA rungs. Our results show that, in contrast with the previous SBH10 study where the BEEF-vdW-DF2 functional seemed to be most accurate, the workhorse functional PBE and the MS2 density functional are the most accurate of the GGA and meta-GGA functionals tested. Of the GGA+vdW functionals tested, the SRP32-vdW-DF1 functional is the most accurate. Additionally, we found that the medium algorithm is accurate enough for assessing the performance of the density functionals tested, while it avoids geometry optimizations of minimum barrier geometries for each density functional tested. The medium algorithm does require metal lattice constants and interlayer distances that are optimized separately for each functional. While these are avoided in the light algorithm, this algorithm is found not to give a reliable description of functional performance. The combination of relative ease of use and demonstrated reliability of the medium algorithm will likely pave the way for incorporation of the SBH17 database in larger databases used for testing new density functionals and electronic structure methods.

Adsorption Energy in Oxygen Electrocatalysis

Adsorption energy (AE) of reactive intermediate is currently the most important descriptor for electrochemical reactions (e.g., water electrolysis, hydrogen fuel cell, electrochemical nitrogen fixation, electrochemical carbon dioxide reduction, etc.), which can bridge the gap between catalyst's structure and activity. Tracing the history and evolution of AE can help to understand electrocatalysis and design optimal electrocatalysts. Focusing on oxygen electrocatalysis, this review aims to provide a comprehensive introduction on how AE is selected as the activity descriptor, the intrinsic and empirical relationships related to AE, how AE links the structure and electrocatalytic performance, the approaches to obtain AE, the strategies to improve catalytic activity by modulating AE, the extrinsic influences on AE from the environment, and the methods in circumventing linear scaling relations of AE. An outlook is provided at the end with emphasis on possible future investigation related to the obstacles existing between adsorption energy and electrocatalytic performance.

Electrosynthesis of ammonia with high selectivity and high rates via engineering of the solid-electrolyte interphase

Ammonia is a large-scale commodity essential to fertilizer production, but the Haber-Bosch process leads to massive emissions of carbon dioxide. Electrochemical ammonia synthesis is an attractive alternative pathway, but the process is still limited by low ammonia production rate and faradaic efficiency. Herein, guided by our theoretical model, we present a highly efficient lithium-mediated process enabled by using different lithium salts, leading to the formation of a uniform solid-electrolyte interphase (SEI) layer on a porous copper electrode. The uniform lithium-fluoride-enriched SEI layer provides an ammonia production rate of 2.5 ± 0.1 μmol s^-1 cm_geo ^-2 at a current density of -1 A cm_geo ^-2 with 71% ± 3% faradaic efficiency under 20 bar nitrogen. Experimental X-ray analysis reveals that the lithium tetrafluoroborate electrolyte induces the formation of a compact and uniform SEI layer, which facilitates homogeneous lithium plating, suppresses the undesired hydrogen evolution as well as electrolyte decomposition, and enhances the nitrogen reduction.

Toward Computing Accurate Free Energies in Heterogeneous Catalysis: a Case Study for Adsorbed Isobutene in H-ZSM-5

Herein, we propose a novel computational protocol that enables calculating free energies with improved accuracy by combining the best available techniques for enthalpy and entropy calculation. While the entropy is described by enhanced sampling molecular dynamics techniques, the energy is calculated using ab initio methods. We apply the method to assess the stability of isobutene adsorption intermediates in the zeolite H-SSZ-13, a prototypical problem that is computationally extremely challenging in terms of calculating enthalpy and entropy. We find that at typical operating conditions for zeolite catalysis (400 °C), the physisorbed π-complex, and not the tertiary carbenium ion as often reported, is the most stable intermediate. This method paves the way for sampling-based techniques to calculate the accurate free energies in a broad range of chemistry-related disciplines, thus presenting a big step forward toward predictive modeling.

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

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

Efficient conversion of low-concentration nitrate sources into ammonia on a Ru-dispersed Cu nanowire electrocatalyst

Electrochemically converting nitrate ions, a widely distributed nitrogen source in industrial wastewater and polluted groundwater, into ammonia represents a sustainable route for both wastewater treatment and ammonia generation. However, it is currently hindered by low catalytic activities, especially under low nitrate concentrations. Here we report a high-performance Ru-dispersed Cu nanowire catalyst that delivers an industrial-relevant nitrate reduction current of 1 A cm^-2 while maintaining a high NH₃ Faradaic efficiency of 93%. More importantly, this high nitrate-reduction catalytic activity enables over a 99% nitrate conversion into ammonia, from an industrial wastewater level of 2,000 ppm to a drinkable water level <50 ppm, while still maintaining an over 90% Faradaic efficiency. Coupling the nitrate reduction effluent stream with an air stripping process, we successfully obtained high purity solid NH₄Cl and liquid NH₃ solution products, which suggests a practical approach to convert wastewater nitrate into valuable ammonia products. Density functional theory calculations reveal that the highly dispersed Ru atoms provide active nitrate reduction sites and the surrounding Cu sites can suppress the main side reaction, the hydrogen evolution reaction.

Limits to scaling relations between adsorption energies?

Linear scaling relations have led to an understanding of trends in catalytic activity and selectivity of many reactions in heterogeneous and electro-catalysis. However, linear scaling between the chemisorption energies of any two small molecule adsorbates is not guaranteed. A prominent example is the lack of scaling between the chemisorption energies of carbon and oxygen on transition metal surfaces. In this work, we show that this lack of scaling originates from different re-normalized adsorbate valence energies of lower-lying oxygen vs higher-lying carbon. We develop a model for chemisorption of small molecule adsorbates within the d-band model by combining a modified form of the Newns-Anderson hybridization energy with an effective orthogonalization term. We develop a general descriptor to a priori determine if two adsorbates are likely to scale with each other.

Performance descriptors of nanostructured metal catalysts for acetylene hydrochlorination

Controlling the precise atomic architecture of supported metals is central to optimizing their catalytic performance, as recently exemplified for nanostructured platinum and ruthenium systems in acetylene hydrochlorination, a key process for vinyl chloride production. This opens the possibility of building on historically established activity correlations. In this study, we derived quantitative activity, selectivity and stability descriptors that account for the metal-dependent speciation and host effects observed in acetylene hydrochlorination. To achieve this, we generated a platform of Au, Pt, Ru, Ir, Rh and Pd single atoms and nanoparticles supported on different types of carbon and assessed their evolution during synthesis and under the relevant reaction conditions. Combining kinetic, transient and chemisorption analyses with modelling, we identified the acetylene adsorption energy as a speciation-sensitive activity descriptor, further determining catalyst selectivity with respect to coke formation. The stability of the different nanostructures is governed by the interplay between single atom-support interactions and chlorine affinity, promoting metal redispersion or agglomeration, respectively.

Structural dynamics of Ru clusters during nitrogen dissociation in ammonia synthesis

The dynamic evolution of catalyst structures greatly influences the reactivity, especially sub-nanometer clusters, exhibiting complex configurational fluctuation. In the present work, we study the structural dynamics of a Ru₁₉ cluster during the dissociation of N₂ and calculate the reaction free energies using ab initio molecular dynamics (AIMD). Our AIMD calculation predicts a peak-shaped reaction entropy curve due to the adsorption-induced phase transition of the Ru₁₉ cluster. The low melting points of sub-nanometer clusters make it possible to activate N₂ at low temperatures. This work demonstrates that the dynamic changes of cluster structures have a non-negligible effect on reaction free energy and offer an opportunity for achieving ammonia synthesis under mild conditions.

The long road to calibrated prediction uncertainty in computational chemistry

Uncertainty quantification (UQ) in computational chemistry (CC) is still in its infancy. Very few CC methods are designed to provide a confidence level on their predictions, and most users still rely improperly on the mean absolute error as an accuracy metric. The development of reliable UQ methods is essential, notably for CC to be used confidently in industrial processes. A review of the CC-UQ literature shows that there is no common standard procedure to report or validate prediction uncertainty. I consider here analysis tools using concepts (calibration and sharpness) developed in meteorology and machine learning for the validation of probabilistic forecasters. These tools are adapted to CC-UQ and applied to datasets of prediction uncertainties provided by composite methods, Bayesian ensembles methods, and machine learning and a posteriori statistical methods.

Green ammonia synthesis using CeO2/RuO2 nanolayers on vertical graphene catalyst via electrochemical route in alkaline electrolyte

The electrochemical synthesis of ammonia at ambient temperature and pressure has the potential to replace the conventional process for the production of ammonia. However, the low ammonia yield and poor long-term stability of catalysts for the synthesis of ammonia hinders the application of this technology. Herein, we endeavored to tackle this challenge by synthesizing 3-D vertical graphene (VG) on Ni foam via a one-step, low-temperature plasma process, which offered high conductivity and large surface area. Subsequently, the vertical graphene on Ni foam was loaded with nanolayers of ruthenium oxide (RuO₂, ∼2 nm) and cerium oxide (CeO₂, <20 nm) nanoparticles via magnetron sputtering. The incorporation of nanoparticle layers (RuO₂ and CeO₂/RuO₂) on VG significantly increased the NH₃ yield in KOH electrolyte. Finally, the performance and long-term stability of this composite material were successfully demonstrated by the addition of CeO₂/RuO₂ nanolayers on the VG electrocatalyst. The catalyst achieved an excellent performance with a high ammonia synthesis yield of 50.56 μg mg_{total cat.}^-1 h^-1 (1.11 × 10^-10 mol cm^-2 s^-1) during the performance evaluation period of 36 h. This observation was also verified by density functional theory calculation, where CeO₂ exhibited the best catalytic performance compared to RuO₂ and pristine graphene.

Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system.

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

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

Dual Active Centers Bridged by Oxygen Vacancies of Ru Single Atoms Hybrids Supported on Molybdenum Oxide for Photocatalytic Ammonia Synthesis

Photocatalytic synthesis of ammonia (NH 3 ) holds significant potential compared with the Haber-Bosch process. However, the reported photocatalysts suffered from low efficiency owing to localized electrons deficiency. Here, Ru-SA (single atoms)/H x MoO 3-y hybrids with abundant of Mo n+ (n < 6) species neighboring oxygen vacancies (O V ) are synthesized via a H-spillover process. Detailed characterizations demonstrate that Ru-SA/H x MoO 3 y hybrids can quantitatively produce NH 3 from N 2 and H 2 by the synergetic effect of dual active centers (Ru SA and Mo n+ ). That is, Ru SA boost the activation and migration of H 2 , and Mo n+ species act as the trapping sites of localized electrons and the adsorption and dissociation sites of N 2 , finally leading to NH 3 synthesis on Mo n+ -OH. The NH 3 generation rate is as high as 4.0 mmol h -1 g -1 , accompanied by an apparent quantum efficiency over 6.0% at 650 nm. Our finding may open up a new strategy for acquiring a better NH 3 synthesis approach under mild conditions.