Emerging Atomistic Modeling Methods for Heterogeneous Electrocatalysis.

Heterogeneous electrocatalysis lies at the center of various technologies that could help enable a sustainable future. However, its complexity makes it challenging to accurately and efficiently model at an atomic level. Here, we review emerging atomistic methods to simulate the electrocatalytic interface with special attention devoted to the components/effects that have been challenging to model, such as solvation, electrolyte ions, electrode potential, reaction kinetics, and pH. Additionally, we review relevant computational spectroscopy methods. Then, we showcase several examples of applying these methods to understand and design catalysts relevant to green hydrogen. We also offer experimental views on how to bridge the gap between theory and experiments. Finally, we provide some perspectives on opportunities to advance the field.

Reduction of Cofed Carbon Dioxide Modifies the Local Coordination Environment of Zeolite-Supported, Atomically Dispersed Chromium to Promote Ethane Dehydrogenation.

The reduction of CO2 is known to promote increased alkene yields from alkane dehydrogenations when the reactions are cocatalyzed. The mechanism of this promotion is not understood in the context of catalyst active-site environments because CO2 is amphoteric, and even general aspects of the chemistry, including the significance of competing side reactions, differ significantly across catalysts. Atomically dispersed chromium cations stabilized in highly siliceous MFI zeolite are shown here to enable the study of the role of parallel CO2 reduction during ethylene-selective ethane dehydrogenation. Based on infrared spectroscopy and X-ray absorption spectroscopy data interpreted through calculations using density functional theory (DFT), the synthesized catalyst contains atomically dispersed Cr cations stabilized by silanol nests in micropores. Reactor studies show that cofeeding CO2 increases stable ethylene-selective ethane dehydrogenation rates over a wide range of partial pressures. Operando X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine-structure (EXAFS) spectra indicate that during reaction at 650 °C the Cr cations maintain a nominal 2+ charge and a total Cr-O coordination number of approximately 2. However, CO2 reduction induces a change, correlated with the CO2 partial pressure, in the population of two distinct Cr-O scattering paths. This indicates that the promotional effect of parallel CO2 reduction can be attributed to a subtle change in Cr-O bond lengths in the local coordination environment of the active site. These insights are made possible by simultaneously fitting multiple EXAFS spectra recorded in different reaction conditions; this novel procedure is expected to be generally applicable for interpreting operando catalysis EXAFS data.

Developing Cheap but Useful Machine Learning-Based Models for Investigating High-Entropy Alloy Catalysts.

This work aims to address the challenge of developing interpretable ML-based models when access to large-scale computational resources is limited. Using CoMoFeNiCu high-entropy alloy catalysts as an example, we present a cost-effective workflow that synergistically combines descriptor-based approaches, machine learning-based force fields, and low-cost density functional theory (DFT) calculations to predict high-quality adsorption energies for H, N, and NHx (x = 1, 2, and 3) adsorbates. This is achieved using three specific modifications to typical DFT workflows including: (1) using a sequential optimization protocol, (2) developing a new geometry-based descriptor, and (3) repurposing the already-available low-cost DFT optimization trajectories to develop a ML-FF. Taken together, this study illustrates how cost-effective DFT calculations and appropriately designed descriptors can be used to develop cheap but useful models for predicting high-quality adsorption energies at significantly lower computational costs. We anticipate that this resource-efficient philosophy may be broadly relevant to the larger surface catalysis community.

A reactive molecular dynamics model for uranium/hydrogen containing systems.

Uranium-based materials are valuable assets in the energy, medical, and military industries. However, understanding their sensitivity to hydrogen embrittlement is particularly challenging due to the toxicity of uranium and the computationally expensive nature of quantum-based methods generally required to study such processes. In this regard, we have developed a Chebyshev Interaction Model for Efficient Simulation (ChIMES) that can be employed to compute energies and forces of U and UH3 bulk structures with vacancies and hydrogen interstitials with accuracy similar to that of Density Functional Theory (DFT) while yielding linear scaling and orders of magnitude improvement in computational efficiency. We show that the bulk structural parameters, uranium and hydrogen vacancy formation energies, and diffusion barriers predicted by the ChIMES potential are in strong agreement with the reference DFT data. We then use ChIMES to conduct molecular dynamics simulations of the temperature-dependent diffusion of a hydrogen interstitial and determine the corresponding diffusion activation energy. Our model has particular significance in studies of actinides and other high-Z materials, where there is a strong need for computationally efficient methods to bridge length and time scales between experiments and quantum theory.

Interconversion of Atomically Dispersed Platinum Cations and Platinum Clusters in Zeolite ZSM-5 and Formation of Platinum gem-Dicarbonyls.

Catalysts composed of platinum dispersed on zeolite supports are widely applied in industry, and coking and sintering of platinum during operation under reactive conditions require their oxidative regeneration, with the platinum cycling between clusters and cations. The intermediate platinum species have remained only incompletely understood. Here, we report an experimental and theoretical investigation of the structure, bonding, and local environment of cationic platinum species in zeolite ZSM-5, which are key intermediates in this cycling. Upon exposure of platinum clusters to O2 at 700 °C, oxidative fragmentation occurs, and Pt2+ ions are stabilized at six-membered rings in the zeolite that contain paired aluminum sites. When exposed to CO under mild conditions, these Pt2+ ions form highly uniform platinum gem-dicarbonyls, which can be converted in H2 to Ptδ+ monocarbonyls. This conversion, which weakens the platinum-zeolite bonding, is a first step toward platinum migration and aggregation into clusters. X-ray absorption and infrared spectra provide evidence of the reductive and oxidative transformations in various gas environments. The chemistry is general, as shown by the observation of platinum gem-dicarbonyls in several commercially used zeolites (ZSM-5, Beta, mordenite, and Y).

Elucidating the Initial Steps in α-Uranium Hydriding Using First-Principles Calculations.

Hydrogen embrittlement of uranium, which arises due to the formation of a structurally weak pyrophoric hydride, poses a major safety risk in material applications. Previous experiments have shown that hydriding begins on the top or near the surface (i.e., subsurface) of α-uranium. However, the fundamental molecular-level mechanism of this process remains unknown. In this work, starting from pristine α-U bulk and surfaces, we present a systematic investigation of possible mechanisms for the formation of metal hydride. Specifically, we address this problem by examining the individual steps of hydrogen embrittlement, including surface adsorption, subsurface absorption, and the interlayer diffusion of atomic hydrogen. Furthermore, by examining these processes across different facets, we highlight the importance of both (1) hydrogen monolayer coverage and (2) applied tensile strain on hydriding kinetics. Taken together, by studying previously overlooked phenomena, this study provides foundational insights into the initial steps of this overall complex process. We anticipate that this work will guide near-term future development of multiscale kinetic models for uranium hydriding and subsequently identify potential strategies to mitigate this undesired process.

A Theory-Guided X-ray Absorption Spectroscopy Approach for Identifying Active Sites in Atomically Dispersed Transition-Metal Catalysts.

Atomically dispersed supported metal catalysts offer new properties and the benefits of maximized metal accessibility and utilization. The characterization of these materials, however, remains challenging. Using atomically dispersed platinum supported on crystalline MgO (chosen for its well-defined bonding sites) as a prototypical example, we demonstrate how systematic density functional theory calculations for assessing all the potentially stable platinum sites, combined with automated analysis of extended X-ray absorption fine structure (EXAFS) spectra, leads to unbiased identification of isolated, surface-enveloped platinum cations as the catalytic species for CO oxidation. The catalyst has been characterized by atomic-resolution imaging and EXAFS and high-energy resolution fluorescence detection X-ray absorption near edge spectroscopy. The proposed platinum sites are in agreement with experiment. This theory-guided workflow leads to rigorously determined structural models and provides a more detailed picture of the structure of the catalytically active site than what is currently possible with conventional EXAFS analyses. As this approach is efficient and agnostic to the metal, support, and catalytic reaction, we posit that it will be of broad interest to the materials characterization and catalysis communities.

Cs-RHO Goes from Worst to Best as Water Enhances Equilibrium CO2 Adsorption via Phase Change.

The strong affinity of water to zeolite adsorbents has made adsorption of CO2 from humid gas mixtures such as flue gas nearly impossible under equilibrated conditions. Here, in this manuscript, we describe a unique cooperative adsorption mechanism between H2O and Cs+ cations on Cs-RHO zeolite, which actually facilitates the equilibrium adsorption of CO2 under humid conditions. Our data demonstrate that, at a relative humidity of 5%, Cs-RHO adsorbs 3-fold higher amounts of CO2 relative to dry conditions, at a temperature of 30 °C and CO2 pressure of 1 bar. A comparative investigation of univalent cation-exchanged RHO zeolites with H+, Li+, Na+, K+, Rb+, and Cs+ shows an increase of equilibrium CO2 adsorption under humid versus dry conditions to be unique to Cs-RHO. In situ powder X-ray diffraction indicates the appearance of a new phase with Im3̅m symmetry after H2O saturation of Cs-RHO. A mixed-cation exchanged NaCs-RHO exhibits similar phase transitions after humid CO2 adsorption; however, we found no evidence of cooperativity between Cs+ and Na+ cations in adsorption, in single-component H2O and CO2 adsorption. We hypothesize based on previous Rietveld refinements of CO2 adsorption in Cs-RHO zeolite that the observed phase change is related to solvation of extra-framework Cs+ cations by H2O. In the case of Cs-RHO, molecular modeling results suggest that hydration of these cations favors their migration from an original D8R position to S8R sites. We posit that this movement enables a trapdoor mechanism by which CO2 can interact with Cs+ at S8R sites to access the α-cage.

Understanding Catalytic Activity Trends in the Oxygen Reduction Reaction.

Despite the dedicated search for novel catalysts for fuel cell applications, the intrinsic oxygen reduction reaction (ORR) activity of materials has not improved significantly over the past decade. Here, we review the role of theory in understanding the ORR mechanism and highlight the descriptor-based approaches that have been used to identify catalysts with increased activity. Specifically, by showing that the performance of the commonly studied materials (e.g., metals, alloys, carbons, etc.) is limited by unfavorable scaling relationships (for binding energies of reaction intermediates), we present a number of alternative strategies that may lead to the design and discovery of more promising materials for ORR.

Understanding trends in C-H bond activation in heterogeneous catalysis.

While the search for catalysts capable of directly converting methane to higher value commodity chemicals and liquid fuels has been active for over a century, a viable industrial process for selective methane activation has yet to be developed. Electronic structure calculations are playing an increasingly relevant role in this search, but large-scale materials screening efforts are hindered by computationally expensive transition state barrier calculations. The purpose of the present letter is twofold. First, we show that, for the wide range of catalysts that proceed via a radical intermediate, a unifying framework for predicting C-H activation barriers using a single universal descriptor can be established. Second, we combine this scaling approach with a thermodynamic analysis of active site formation to provide a map of methane activation rates. Our model successfully rationalizes the available empirical data and lays the foundation for future catalyst design strategies that transcend different catalyst classes.

Nature of Lone-Pair-Surface Bonds and Their Scaling Relations.

We investigate the (surface) bonding of a class of industrially and biologically important molecules in which the chemically active orbital is a 2 p electron lone pair located on an N or O atom bound via single bonds to H or alkyl groups. This class includes water, ammonia, alcohols, ethers, and amines. Using extensive density functional theory (DFT) calculations, we discover scaling relations (correlations) among molecular binding energies of different members of this class: the bonding energetics of a single member can be used as a descriptor for other members. We investigate the bonding mechanism for a representative (H2O) and find the most important physical surface properties that dictate the strength and nature of the bonding through a combination of covalent and noncovalent electrostatic effects. We describe the importance of surface intrinsic electrostatic, geometric, and mechanical properties in determining the extent of the lone-pair-surface interactions. We study systems including ionic materials in which the surface positive and negative centers create strong local surface electric fields, which polarize the dangling lone pair and lead to a strong "electrostatically driven bond". We emphasize the importance of noncovalent electrostatic effects and discuss why a fully covalent picture, common in the current first-principles literature on surface bonding of these molecules, is not adequate to correctly describe the bonding mechanism and energy trends. By pointing out a completely different mechanism (charge transfer) as the major factor for binding N- and O-containing unsaturated (radical) adsorbates, we explain why their binding energies can be tuned independently from those of the aforementioned species, having potential implications in scaling-driven catalyst discovery.

Mechanistic insights into heterogeneous methane activation.

While natural gas is an abundant chemical fuel, its low volumetric energy density has prompted a search for catalysts able to transform methane into more useful chemicals. This search has often been aided through the use of transition state (TS) scaling relationships, which estimate methane activation TS energies as a linear function of a more easily calculated descriptor, such as final state energy, thus avoiding tedious TS energy calculations. It has been shown that methane can be activated via a radical or surface-stabilized pathway, both of which possess a unique TS scaling relationship. Herein, we present a simple model to aid in the prediction of methane activation barriers on heterogeneous catalysts. Analogous to the universal radical TS scaling relationship introduced in a previous publication, we show that a universal TS scaling relationship that transcends catalysts classes also seems to exist for surface-stabilized methane activation if the relevant final state energy is used. We demonstrate that this scaling relationship holds for several reducible and irreducible oxides, promoted metals, and sulfides. By combining the universal scaling relationships for both radical and surface-stabilized methane activation pathways, we show that catalyst reactivity must be considered in addition to catalyst geometry to obtain an accurate estimation for the TS energy. This model can yield fast and accurate predictions of methane activation barriers on a wide range of catalysts, thus accelerating the discovery of more active catalysts for methane conversion.

DFT-Derived Force Fields for Modeling Hydrocarbon Adsorption in MIL-47(V).

Generic force fields such as UFF and DREIDING are widely used for predicting molecular adsorption and diffusion in metal-organic frameworks (MOFs), but the accuracy of these force fields is unclear. We describe a general framework for developing transferable force fields for modeling the adsorption of alkanes in a nonflexible MIL-47(V) MOF using periodic density functional theory (DFT) calculations. By calculating the interaction energies for a large number of energetically favorable adsorbate configurations using DFT, we obtain a force field that gives good predictions of adsorption isotherms, heats of adsorption, and diffusion properties for a wide range of alkanes and alkenes in MIL-47(V). The force field is shown to be transferable to related materials such as MIL-53(Cr) and is used to calculate the free-energy differences for the experimentally observed phases of MIL-53(Fe).

Toward an ab Initio Description of Adsorbate Surface Dynamics.

The advent of machine learning potentials (MLPs) provides a unique opportunity to access simulation time scales and to directly compute physicochemical properties that are typically intractable using density functional theory (DFT). In this study, we use an active learning curriculum to train a generalizable MLP using the DeepMD-kit architecture. By using sufficiently long MLP-based molecular dynamics (MD) simulations, which provide DFT-level accuracy, we investigate the diffusion of key surface-bound adsorbates on a Ag(111) facet. Detailed analysis of the MLP/MD-calculated diffusivities sheds light on the potential shortcomings of using DFT-based nudged elastic band to estimate surface diffusion barriers. More generally, while this study is focused on a specific system, we anticipate that the underlying workflows and the resulting models can be extended to other adsorbates and other materials in the future.

Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training.

Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu]2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu]2+-catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and, subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step toward overcoming the long-standing barriers within the computational heterogeneous catalysis community.

Real-time visual intelligence for defect detection in pharmaceutical packaging.

Defect detection in pharmaceutical blister packages is the most challenging task to get an accurate result in detecting defects that arise in tablets while manufacturing. Conventional defect detection methods include human intervention to check the quality of tablets within the blister packages, which is inefficient, time-consuming, and increases labor costs. To mitigate this issue, the YOLO family is primarily used in many industries for real-time defect detection in continuous production. To enhance the feature extraction capability and reduce the computational overhead in a real-time environment, the CBS-YOLOv8 is proposed by enhancing the YOLOv8 model. In the proposed CBS-YOLOv8, coordinate attention is introduced to improve the feature extraction capability by capturing the spatial and cross-channel information and also maintaining the long-range dependencies. The BiFPN (weighted bi-directional feature pyramid network) is also introduced in YOLOv8 to enhance the feature fusion at each convolution layer to avoid more precise information loss. The model's efficiency is enhanced through the implementation of SimSPPF (simple spatial pyramid pooling fast), which reduces computational demands and model complexity, resulting in improved speed. A custom dataset containing defective tablet images is used to train the proposed model. The performance of the CBS-YOLOv8 model is then evaluated by comparing it with various other models. Experimental results on the custom dataset reveal that the CBS-YOLOv8 model achieves a mAP of 97.4% and an inference speed of 79.25 FPS, outperforming other models. The proposed model is also evaluated on SESOVERA-ST saline bottle fill level monitoring dataset achieved the mAP50 of 99.3%. This demonstrates that CBS-YOLOv8 provides an optimized inspection process, enabling prompt detection and correction of defects, thus bolstering quality assurance practices in manufacturing settings.

Stability and Dynamics of Zeolite-Confined Gold Nanoclusters.

Nanoengineered metal@zeolite materials have recently emerged as a promising class of catalysts for several industrially relevant reactions. These materials, which consist of small transition metal nanoclusters confined within three-dimensional zeolite pores, are interesting because they show higher stability and better sintering resistance under reaction conditions. While several such hybrid catalysts have been reported experimentally, key questions such as the impact of the zeolite frameworks on the properties of the metal clusters are not well understood. To address such knowledge gaps, in this study, we report a robust and transferable machine learning-based potential (MLP) that is capable of describing the structure, stability, and dynamics of zeolite-confined gold nanoclusters. Specifically, we show that the resulting MLP maintains ab initio accuracy across a range of temperatures (300-1000 K) and can be used to investigate time scales (>10 ns), length scales (ca. 10,000 atoms), and phenomena (e.g., ensemble-averaged stability and diffusivity) that are typically inaccessible using density functional theory (DFT). Taken together, this study represents an important step in enabling the rational theory-guided design of metal@zeolite catalysts.

Reversible Intrapore Redox Cycling of Platinum in Platinum-Ion-Exchanged HZSM-5 Catalysts.

Isolated platinum(II) ions anchored at acid sites in the pores of zeolite HZSM-5, initially introduced by aqueous ion exchange, were reduced to form platinum nanoparticles that are stably dispersed with a narrow size distribution (1.3 ± 0.4 nm in average diameter). The nanoparticles were confined in reservoirs within the porous zeolite particles, as shown by electron beam tomography and the shape-selective catalysis of alkene hydrogenation. When the nanoparticles were oxidatively fragmented in dry air at elevated temperature, platinum returned to its initial in-pore atomically dispersed state with a charge of +2, as shown previously by X-ray absorption spectroscopy. The results determine the conditions under which platinum is retained within the pores of HZSM-5 particles during redox cycles that are characteristic of the reductive conditions of catalyst operation and the oxidative conditions of catalyst regeneration.

Dynamic structural evolution of MgO-supported palladium catalysts: from metal to metal oxide nanoparticles to surface then subsurface atomically dispersed cations.

Supported noble metal catalysts, ubiquitous in chemical technology, often undergo dynamic transformations between reduced and oxidized states-which influence the metal nuclearities, oxidation states, and catalytic properties. In this investigation, we report the results of in situ X-ray absorption spectroscopy, scanning transmission electron microscopy, and other physical characterization techniques, bolstered by density functional theory, to elucidate the structural transformations of a set of MgO-supported palladium catalysts under oxidative treatment conditions. As the calcination temperature increased, the as-synthesized supported metallic palladium nanoparticles underwent oxidation to form palladium oxides (at approximately 400 °C), which, at approximately 500 °C, were oxidatively fragmented to form mixtures of atomically dispersed palladium cations. The data indicate two distinct types of atomically dispersed species: palladium cations located at MgO steps and those embedded in the first subsurface layer of MgO. The former exhibit significantly higher (>500 times) catalytic activity for ethylene hydrogenation than the latter. The results pave the way for designing highly active and stable supported palladium hydrogenation catalysts with optimized metal utilization.

Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials.

Machine learning potentials (MLPs) capable of accurately describing complex ab initio potential energy surfaces (PESs) have revolutionized the field of multiscale atomistic modeling. In this work, using an extensive density functional theory (DFT) data set (denoted as Si-ZEO22) consisting of 219 unique zeolite topologies (350,000 unique DFT calculations) found in the International Zeolite Association (IZA) database, we have trained a DeePMD-kit MLP to model the dynamics of silica frameworks. The performance of our model is evaluated by calculating various properties that probe the accuracy of the energy and force predictions. This MLP demonstrates impressive agreement with DFT for predicting zeolite structural properties, energy-volume trends, and phonon density of states. Furthermore, our model achieves reasonable predictions for stress-strain relationships without including DFT stress data during training. These results highlight the ability of MLPs to capture the flexibility of zeolite frameworks and motivate further MLP development for nanoporous materials with near-ab initio accuracy.