Computational Methods in Heterogeneous Catalysis

LSPR-enhanced photoresponsive antibacterial efficiency of Bi/MoS2-loaded fibrin gel for management of diabetic wounds

Chronic diabetic wounds present formidable challenges, marked by uncontrolled bacterial infections, prolonged inflammation, and impaired angiogenesis. The evolving landscape of photo-responsive antibacterial therapy holds great promise in addressing these multifaceted issues, with a particular focus on leveraging the distinctive properties of 2D heterojunction materials. In this investigation, we engineered composite sprayed hydrogels, seamlessly integrating Bi/MoS2 nano-heterojunctions. Capitalizing on the synergistic interplay between photocatalytic antibacterial and photothermal antibacterial mechanisms, the Bi/MoS2 heterojunction, guided by its localized surface plasmon resonance, demonstrated outstanding antibacterial efficacy within a mere 10-minute exposure to 808 nm near-infrared light. This accelerated sterilization both in vitro and in vivo, consequently expediting wound healing. The sprayed composite gel not only furnishes protective shielding for skin tissues but also fosters endothelial cell proliferation, vascularization, and angiogenesis. This safe and ultrafast sterilizing hydrogel presents immense potential for application in antimicrobial dressings, thereby offering a promising avenue for diabetic wound healing.

Accelerating Relativistic Exact-Two-Component Density Functional Theory Calculations with Graphical Processing Units

Numerical integration of the exchange-correlation potential is an inherently parallel problem that can be significantly accelerated by graphical processing units (GPUs). In this Letter, we present the first implementation of GPU-accelerated exchange-correlation potential in the GauXC library for relativistic, 2-component density functional theory. By benchmarking against copper, silver, and gold coinage metal clusters, we demonstrate the speed and efficiency of our implementation, achieving significant speedup compared to CPU-based calculations. One GPU card provides computational power equivalent to roughly 400 CPU cores in the context of this work. The speedup further increases for larger systems, highlighting the potential of our approach for future, more computationally demanding simulations. Our implementation supports arbitrary angular momentum basis functions, enabling the simulation of systems with heavy elements and providing substantial speedup to relativistic electronic structure calculations. This advancement paves the way for more efficient and extensive computational studies in the field of density functional theory.

Ichor: A Python library for computational chemistry data management and machine learning force field development

We present ichor, an open-source Python library that simplifies data management in computational chemistry and streamlines machine learning force field development. Ichor implements many easily extensible file management tools, in addition to a lazy file reading system, allowing efficient management of hundreds of thousands of computational chemistry files. Data from calculations can be readily stored into databases for easy sharing and post-processing. Raw data can be directly processed by ichor to create machine learning-ready datasets. In addition to powerful data-related capabilities, ichor provides interfaces to popular workload management software employed by High Performance Computing clusters, making for effortless submission of thousands of separate calculations with only a single line of Python code. Furthermore, a simple-to-use command line interface has been implemented through a series of menu systems to further increase accessibility and efficiency of common important ichor tasks. Finally, ichor implements general tools for visualization and analysis of datasets and tools for measuring machine-learning model quality both on test set data and in simulations. With the current functionalities, ichor can serve as an end-to-end data procurement, data management, and analysis solution for machine-learning force-field development.

Experimental and computational aspects of molecular frustrated Lewis pairs for CO2 hydrogenation: en route for heterogeneous systems?

Catalysis plays a crucial role in advancing sustainability. The unique reactivity of frustrated Lewis pairs (FLPs) is driving an ever-growing interest in the transition metal-free transformation of small molecules like CO2 into valuable products. In this area, there is a recent growing incentive to heterogenize molecular FLPs into porous solids, merging the benefits of homogeneous and heterogeneous catalysis - high activity, selectivity, and recyclability. Despite the progress, challenges remain in preventing deactivation, poisoning, and simplifying catalyst-product separation. This review explores the expanding field of FLPs in catalysis, covering existing molecular FLPs for CO2 hydrogenation and recent efforts to design heterogeneous porous systems from both experimental and theoretical perspectives. Section 2 discusses experimental examples of CO2 hydrogenation by molecular FLPs, starting with stoichiometric reactions and advancing to catalytic ones. It then examines attempts to immobilize FLPs in porous matrices, including siliceous solids, metal-organic frameworks (MOFs), covalent organic frameworks, and disordered polymers, highlighting current limitations and challenges. Section 3 then reviews computational studies on the mechanistic details of CO2 hydrogenation, focusing on H2 splitting and hydride/proton transfer steps, summarizing efforts to establish structure-activity relationships. It also covers the computational aspects on grafting FLPs inside MOFs. Finally, Section 4 summarizes the main design principles established so far, while addressing the complexities of translating computational approaches into the experimental realm, particularly in heterogeneous systems. This section underscores the need to strengthen the dialogue between theoretical and experimental approaches in this field.

Data-Driven Structure Recognition of Scanning Tunneling Microscopy Images in a Case of Iron Carbide

Scanning tunneling microscopy (STM) serves as a critical tool for high-resolution surface imaging, yet deciphering the atomic structures from STM images on multielement surfaces, such as oxides and carbides, remains a challenging task that heavily relies on the expertise and intuition of researchers. In this study, we introduce a data-driven method for rapid structural recognition from STM images. This method involves extracting structural features, filtering through a structural database, and matching with simulated STM images and surface energy analyses, thereby providing researchers with several of the most probable structures. We demonstrate the capabilities of this technique using our previously reported iron carbide grown on an Fe(110) crystal. By proposing a candidate structure set and establishing a comprehensive database linking STM images to corresponding structures and surface energies, we selected 6 out of more than 10 000 possible surfaces. On the basis of these 6 recommendations, researchers can conveniently determine the real surface structures. Our work provides an efficient tool for the structure recognition of STM images to construct surface structures, potentially serving as a universal auxiliary tool for STM structural analysis.

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.

Dynamics of Elementary Steps on Metal Surfaces at High Coverages: The Prevalence and Kinetic Competence of Contiguous Bare-Atom Ensembles

The rate of elementary steps on densely-covered surfaces depends sensitively on repulsive interactions within dense adlayers, situations ubiquitous in practice and with kinetic consequences seldom captured by Langmuirian treatments of surface catalysis. This study develops an ensemble-based method that assesses how such repulsion influences the prevalence and kinetic competence of bare-atom ensembles of different size. Chemisorbed CO (CO*) is used as an example because it forms dense adlayers on metal nanoparticles during CO2 hydrogenation (CO2-H2) and other reactions, leading to significant repulsion that weakens the binding of CO* and kinetically-relevant transition states (TS). This approach is enabled by density functional theory and probability formalisms and describes the prevalence of ensembles of contiguous bare atoms from their formation energy (via CO* desorption); it then determines their competence in stabilizing the TS and mediating the reaction rates. The specific conclusions reflect the extent to which a given TS and CO* desorbed to form bare ensembles "sense" repulsion and the contribution of each ensemble size to each reaction channel mediated by distinct TS structures. These formalisms are illustrated by assessing the relative contributions, kinetic relevance, and ensemble size requirements for two CO2-H2 routes (direct and H-assisted CO2 activation to CO and H2O) on Ru nanoparticles, but they are not restricted to specific bound species or reaction channels. This method is essential to assess the kinetic relevance of elementary steps in a given catalytic sequence and to determine the contributions from parallel reaction channels at the crowded surfaces that prevail in the practice of surface catalysis.

Electron Delocalization and Transfer Across Polyoxometalates-Based Subnanomaterials in Catalytic Reactions

Due to their identical building blocks and high surface-to-volume ratio, subnanomaterials exhibit significant properties compared to their bulk nanomaterial counterparts. The interactions between these building blocks can result in either equal or unequal sharing of electrons, leading to electron transfer in heterojunctions or electron delocalization within symmetric structures. Clusters, possessing electronic properties akin to atoms, can serve as reservoirs of electrons to stabilize crucial intermediates in catalytic reactions. This perspective provides a novel understanding of well-defined subnanomaterials with distinct architectures, such as cluster-based constructions and co-assembled heterojunctions, emphasizing the relationship between electronic structures and catalytic properties. The objective is to provide novel perspectives on the realm of subnanomaterials and cluster-based architectures.

A theory of phonon-induced friction on molecular adsorbates

In this manuscript, we provide a general theory for how surface phonons couple to molecular adsorbates. Our theory maps the extended dynamics of a surface's atomic vibrational motions to a generalized Langevin equation, and by doing so captures these dynamics in a single quantity: the non-Markovian friction. The different frequency components of this friction are the phonon modes of the surface slab weighted by their coupling to the adsorbate degrees of freedom. Using this formalism, we demonstrate that physisorbed species couple primarily to acoustic phonons while chemisorbed species couple to dispersionless local vibrations. We subsequently derive equations for phonon-adjusted reaction rates using transition state theory and demonstrate that these corrections improve agreement with experimental results for CO desorption rates from Pt(111).

Enabling high throughput deep reinforcement learning with first principles to investigate catalytic reaction mechanisms

Exploring catalytic reaction mechanisms is crucial for understanding chemical processes, optimizing reaction conditions, and developing more effective catalysts. We present a reaction-agnostic framework based on high-throughput deep reinforcement learning with first principles (HDRL-FP) that offers excellent generalizability for investigating catalytic reactions. HDRL-FP introduces a generalizable reinforcement learning representation of catalytic reactions constructed solely from atomic positions, which are subsequently mapped to first-principles-derived potential energy landscapes. By leveraging thousands of simultaneous simulations on a single GPU, HDRL-FP enables rapid convergence to the optimal reaction path at a low cost. Its effectiveness is demonstrated through the studies of hydrogen and nitrogen migration in Haber-Bosch ammonia synthesis on the Fe(111) surface. Our findings reveal that the Langmuir-Hinshelwood mechanism shares the same transition state as the Eley-Rideal mechanism for H migration to NH2, forming ammonia. Furthermore, the reaction path identified herein exhibits a lower energy barrier compared to that through nudged elastic band calculation.

Comparative simulations of methanol steam reforming on PdZn alloy using kinetic Monte Carlo and mean-field microkinetic model

Methanol steam reforming (MSR) is an attractive route for producing clean energy hydrogen. PdZn alloys are extensively studied as potential MSR catalysts for their stability and high CO2 selectivity. Here, we investigated the reaction mechanism using density functional calculations, mean-field microkinetic modeling (MF-MKM), and kinetic Monte Carlo (kMC) simulations. To overcome the over-underestimation of CO2 selectivity by log-kMC, an ads-kMC algorithm is proposed in which the adsorption/desorption rate constants were reduced under certain requirements and the diffusion process was treated by redistributing surface species each time an event occured. The simulations show that the dominant pathway to CO2 at low temperatures is CH3OH → CH3O → CH2O → H2COOH → H2COO → HCOO → CO2. The ads-kMC predicted OH coverage is 2-3 times that of MF-MKM, while they produce similar coverage for other species. Analyses indicate that surface OH promotes the dehydrogenation of CH3OH, CH3O, and H2COOH significantly and plays a key role in the MSR process. The dissociation of water/methanol is the most important rate-limiting/rate-inhibiting step. The CO2 selectivity obtained by the two methods is close to each other and consistent with the experimental trend with temperature. Generally, the ads-kMC results agree with the MF-MKM ones, supporting the previous finding that kMC and MF-MKM predict similar results if the diffusion is very fast and adsorbate interactions are neglected. The present study sheds light on the MSR process on PdZn alloys, and the proposed scheme to overcome the stiff problems in kMC simulations is worthy of being extended to other systems.

Dry Reforming of Methane on Ni/LaZrO2 Catalyst under External Electric Fields: A Combined First-Principles and Microkinetic Modeling Study

Dry reforming of methane (DRM) reaction has great potential in reducing the greenhouse effect and solving energy problems. Herein, the DRM reaction mechanism and activity on Ni16/LaZrO2 catalyst under electric fields were comprehensively investigated by combining density functional theory calculations with microkinetic modeling. The results showed that La doping increases the interaction between Ni and ZrO2 by Ni cluster transfer of more electrons. The adsorption strength of species followed the order Ni16/ZrO2 > Ni16/LaZrO2, which is consistent with the results for the d-band center but opposite to the metal-support interaction. The best DRM reaction path on Ni16/LaZrO2 was the CH2-O pathway, which is different from the CH-O pathway on Ni(111) and Ni16/ZrO2. Both positive and negative electric fields of strong and weak metal-support interactions reduced the energy barrier of DRM reaction. Importantly, our results showed that the more dispersed and smaller Ni12/LaZrO2 model by considering the dispersing effect induced by La doping, which displayed very different results from that of Ni16/LaZrO2: reduced the energy barrier for methane decomposition, thereby promoting DRM reaction activity. Microkinetic results showed that the carbon deposition behavior of DRM becomes weaker on Ni16/LaZrO2 due to the suppression of methane decomposition in the presence of La doping compared to Ni16/ZrO2, but the opposite result is obtained on Ni12/LaZrO2. The order of DRM reactivity was Ni16/LaZrO2 < Ni16/ZrO2 < Ni12/LaZrO2, which is consistent with the experiment observations. The conversion of methane and CO2 was higher in positive electric fields than in negative electric fields at low temperatures, but the results were opposite at high temperature. Negative electric fields can improve the carbon deposition resistance of Ni-based catalysts compared to positive electric fields. The degree of rate control analysis showed that CHx* oxidation also plays an important role in the DRM reaction. We envision that this study could provide a deeper understanding for guiding the widespread application of electric field catalysis.

The Importance of Reaction Energy in Predicting Chemical Reaction Barriers with Machine Learning Models

Improving our fundamental understanding of complex heterocatalytic processes increasingly relies on electronic structure simulations and microkinetic models based on calculated energy differences. In particular, calculation of activation barriers, usually achieved through compute-intensive saddle point search routines, remains a serious bottleneck in understanding trends in catalytic activity for highly branched reaction networks. Although the well-known Brønsted-Evans-Polyani (BEP) scaling - a one-feature linear regression model - has been widely applied in such microkinetic models, they still rely on calculated reaction energies and may not generalize beyond a single facet on a single class of materials, e. g., a terrace sites on transition metals. For highly branched and energetically shallow reaction networks, such as electrochemical CO2 reduction or wastewater remediation, calculating even reaction energies on many surfaces can become computationally intractable due to the combinatorial explosion of states that must be considered. Here, we investigate the feasibility of activation barrier prediction without knowledge of the reaction energy using linear and nonlinear machine learning (ML) models trained on a new database of over 500 dehydrogenation activation barriers. We also find that inclusion of the reaction energy significantly improves both classes of ML models, but complex nonlinear models can achieve performance similar to the simplest BEP scaling when predicting activation barriers on new systems. Additionally, inclusion of the reaction energy significantly improves generalizability to new systems beyond the training set. Our results suggest that the reaction energy is a critical feature to consider when building models to predict activation barriers, indicating that efforts to reliably predict reaction energies through, e. g., the Open Catalyst Project and others, will be an important route to effective model development for more complex systems.

On-the-fly kinetic Monte Carlo simulations with neural network potentials for surface diffusion and reaction

We develop an adaptive scheme in the kinetic Monte Carlo simulations, where the adsorption and activation energies of all elementary steps, including the effects of other adsorbates, are evaluated "on-the-fly" by employing the neural network potentials. The configurations and energies evaluated during the simulations are stored for reuse when the same configurations are sampled in a later step. The present scheme is applied to hydrogen adsorption and diffusion on the Pd(111) and Pt(111) surfaces and the CO oxidation reaction on the Pt(111) surface. The effects of interactions between adsorbates, i.e., adsorbate-adsorbate lateral interactions, are examined in detail by comparing the simulations without considering lateral interactions. This study demonstrates the importance of lateral interactions in surface diffusion and reactions and the potential of our scheme for applications in a wide variety of heterogeneous catalytic reactions.

Dynamic Active Sites In Situ Formed in Metal Nanoparticle Reshaping under Reaction Conditions

Understanding the nonequilibrium transformation of nanocatalysts under reaction conditions is important because metastable atomic structures may be created during the process, which offers unique activities in reactions. Although reshaping of metal nanoparticles (NPs) under reaction conditions has been widely recognized, the dynamic reshaping process has been less studied at the atomic scale. Here, we develop an atomistic kinetic Monte Carlo model to simulate the complete reshaping process of Pt nanoparticles in a CO environment and reveal the in situ formation of atomic clusters on the NP surface, a new type of active site beyond conventional understanding, boosting the reactivities in the CO oxidation reaction. Interestingly, highly active peninsula and inactive island clusters both form on the (111) facets and interchange in varying states of dynamic equilibrium, which influences the catalytic activities significantly. This study provides new fundamental knowledge of nanocatalysis and new guidance for the rational design of nanocatalysts.

Selective Adsorbent Design with Multifunctional Surfaces: Innovating Solutions for Heterogeneous Catalysis in Water

Heterogeneous catalytic systems with water as the solvent often have the disadvantage of cross-contamination, while concerns about the purification and workup of the aqueous phase after reactions are rare in the lab or industry. In this context, designing and developing the functional selective solid adsorbent and revealing the adsorption mechanism can provide a new strategy and guidelines for constructing supported heterogeneous catalysts to address these issues. Herein, we report the stable composite adsorbent (Fe/ATP@PPy: magnetic Fe3O4/attapulgite with the polypyrrole shell) that features an integrated multifunctional surface, which can effectively tune the selective adsorption processes for Cu2+, Co2+, and Ni2+ ions and nitrobenzene via the cooperative chemisorption/physisorption in an aqueous system. The adsorption experiments showed that Fe/ATP@PPy displayed significantly higher adsorption selectivity for Ni2+ than Cu2+ and Co2+ ions, especially which exhibited an approximate 100.00% removal for both Ni2+ ions and nitrobenzene in the mixture system with a low concentration. Furthermore, combined tracking adsorption of Ni2+ ions and X-ray photoelectron spectroscopy characterization confirmed that the effective adsorption occurs via ion transfer coordination; the pathway was further validated at the molecular level through theoretical modeling. In addition, the selective adsorption mechanism was proposed based on the adsorption experiment, characterization, and the corresponding density functional theory calculation.

Spontaneous Charge Separation at the Metal-Water Interface

Reactions at the metal-water interface are essential in a range of fundamental and technological processes. Using Density Functional Theory calculations, we demonstrate that water substantially affects the adsorption of H and O2 on Cu(111), Ag(111), Au(111), Pd(111) and Pt(111). In water, H is found to undergo a spontaneous charge separation, where a proton desorbs to the water solution while an electron is donated to the surface. The reaction is exothermic over Au and Pt and associated with low barriers. The process is facile also over Pd, albeit slightly endothermic. For O2, water is found to increase the metal-to-adsorbate charge transfer, enhancing the adsorption energy and O-O bond length as compared to the adsorption in the absence of water. The magnitudes of the effects are system dependent, which implies that calculations should treat water explicitly. The results elucidate previous experimental results and highlights the importance of charge-transfer effects at the metal-water interface; both to describe the potential energy landscape, and to account for alternative reaction routes in the presence of water.

Interpreting chemisorption strength with AutoML-based feature deletion experiments

The chemisorption energy of reactants on a catalyst surface, [Formula: see text], is among the most informative characteristics of understanding and pinpointing the optimal catalyst. The intrinsic complexity of catalyst surfaces and chemisorption reactions presents significant difficulties in identifying the pivotal physical quantities determining [Formula: see text]. In response to this, the study proposes a methodology, the feature deletion experiment, based on Automatic Machine Learning (AutoML) for knowledge extraction from a high-throughput density functional theory (DFT) database. The study reveals that, for binary alloy surfaces, the local adsorption site geometric information is the primary physical quantity determining [Formula: see text], compared to the electronic and physiochemical properties of the catalyst alloys. By integrating the feature deletion experiment with instance-wise variable selection (INVASE), a neural network-based explainable AI (XAI) tool, we established the best-performing feature set containing 21 intrinsic, non-DFT computed properties, achieving an MAE of 0.23 eV across a periodic table-wide chemical space involving more than 1,600 types of alloys surfaces and 8,400 chemisorption reactions. This study demonstrates the stability, consistency, and potential of AutoML-based feature deletion experiment in developing concise, predictive, and theoretically meaningful models for complex chemical problems with minimal human intervention.

Computational chemistry for water-splitting electrocatalysis

Electrocatalytic water splitting driven by renewable electricity has attracted great interest in recent years for producing hydrogen with high-purity. However, the practical applications of this technology are limited by the development of electrocatalysts with high activity, low cost, and long durability. In the search for new electrocatalysts, computational chemistry has made outstanding contributions by providing fundamental laws that govern the electron behavior and enabling predictions of electrocatalyst performance. This review delves into theoretical studies on electrochemical water-splitting processes. Firstly, we introduce the fundamentals of electrochemical water electrolysis and subsequently discuss the current advancements in computational methods and models for electrocatalytic water splitting. Additionally, a comprehensive overview of benchmark descriptors is provided to aid in understanding intrinsic catalytic performance for water-splitting electrocatalysts. Finally, we critically evaluate the remaining challenges within this field.

High-Throughput Screening of Sulfur-Resistant Catalysts for Steam Methane Reforming Using Machine Learning and Microkinetic Modeling

The catalytic activity of bimetallic catalysts for the steam methane reforming (SMR) reaction was extensively studied previously. However, the performance of these materials in the presence of sulfur-containing species is yet to be investigated. In this study, we propose a novel process aided by machine learning (ML) and microkinetic modeling for the rapid screening of sulfur-resistant bimetallic catalysts. First, various ML models were developed to predict atomic adsorption energies (C, H, O, and S) on bimetallic surfaces. Easily accessible physical and chemical properties of the metals and adsorbates were used as input features. The Ensemble learning, artificial neural network, and support vector regression models achieved the best performance with R2 values of 0.74, 0.71, and 0.70, respectively. A microkinetic model was then built based on the elementary steps of the SMR reaction. Finally, the microkinetic model, together with the atomic adsorption energies predicted by the Ensemble model, were used to screen over 500 bimetallic materials. Four Ge-based alloys (Ge3Cu1, Ge3Ni1, Ge3Co1, and Ge3Fe1) and the Ni3Cu1 alloy were identified as promising and cost-effective sulfur-resistant catalysts.

Mechanisms of CH4 activation over oxygen-preadsorbed transition metals by ReaxFF and AIMD simulations

The chemisorbed oxygen usually promotes the CH bond activation over less active metals like IB group metals but has no effect or even an inhibition effect over more active metals like Pd based on the static electronic structure study. However, the understanding in terms of dynamics knowledge is far from complete. In the present work, methane dissociation on the oxygen-preadsorbed transition metals including Au, Cu, Ni, Pt, and Pd is systemically studied by reactive force field (ReaxFF). The ReaxFF simulation results indicate that CH4 molecules mainly undergo the direct dissociation on Ni, Pt, and Pd surfaces, while undergo the oxygen-assisted dissociation on Au and Cu surfaces. Additionally, the ab initio molecular dynamics (AIMD) simulations with the umbrella sampling are employed to study the free-energy changes of CH4 dissociation, and the results further support the CH4 dissociation pathway during the ReaxFF simulations. The present results based on ReaxFF and AIMD will provide a deeper dynamic understanding of the effects of pre-adsorbed oxygen species on the CH bond activation compared to that of static DFT.

ChatGPT in the Material Design: Selected Case Studies to Assess the Potential of ChatGPT

The pursuit of designing smart and functional materials is of paramount importance across various domains, such as material science, engineering, chemical technology, electronics, biomedicine, energy, and numerous others. Consequently, researchers are actively involved in the development of innovative models and strategies for material design. Recent advancements in analytical tools, experimentation, and computer technology additionally enhance the material design possibilities. Notably, data-driven techniques like artificial intelligence and machine learning have achieved substantial progress in exploring various applications within material science. One such approach, ChatGPT, a large language model, holds transformative potential for addressing complex queries. In this article, we explore ChatGPT's understanding of material science by assigning some simple tasks across various subareas of computational material science. The findings indicate that while ChatGPT may make some minor errors in accomplishing general tasks, it demonstrates the capability to learn and adapt through human interactions. However, issues like output consistency, probable hidden errors, and ethical consequences should be addressed.

Reference-Quality Free Energy Barriers in Catalysis from Machine Learning Thermodynamic Perturbation Theory

For the first time, we report calculations of the free energies of activation of cracking and isomerization reactions of alkenes that combine several different electronic structure methods with molecular dynamics simulations. We demonstrate that the use of a high level of theory (here Random Phase Approximation-RPA) is necessary to bridge the gap between experimental and computed values. These transformations, catalyzed by zeolites and proceeding via cationic intermediates and transition states, are building blocks of many chemical transformations for valorization of long chain paraffins originating, e.g., from plastic waste, vegetable oils, Fischer-Tropsch waxes or crude oils. Compared with the free energy barriers computed at the PBE+D2 production level of theory via constrained ab initio molecular dynamics, the barriers computed at the RPA level by the application of Machine Learning thermodynamic Perturbation Theory (MLPT) show a significant decrease for isomerization reaction and an increase of a similar magnitude for cracking, yielding an unprecedented agreement with the results obtained by experiments and kinetic modeling.

Level-Shifted Embedded Cluster Method for Modeling the Chemistry of Metal Oxides

The embedded cluster method has been used extensively in the study of the chemical and physical properties of metal oxides. This method has been a popular tool due to its relatively high accuracy and low computational cost. An even more promising option may entail integrating the embedded cluster method with the combined quantum mechanical and molecular mechanical (QM/MM) approach, thereby enabling further consideration of interactions within the entire system for superior results. We aim to accurately model the chemistry of metal oxides using this combined scheme. Here, using the prototypical MgO(100) surface as a test system, with Mg9O14 as the cluster in the quantum mechanical region, we show that the embedded cluster with untailored boundary effective core potentials (ECPs) can have frontier orbital energy levels that substantially deviate from the quantum mechanical reference results. This occurs even when Mg9O9, which retains the stoichiometry of MgO, is used as the cluster in the quantum mechanical region. As a result, the chemical properties of the embedded cluster models differ from those of the quantum mechanical reference model. To address this issue, we propose a new variant of the embedded cluster method called the level-shifted embedded cluster (LSEC) method, which allows the energy levels to be shifted to match the reference levels by tuning the boundary ECPs. Our validation calculations on the adsorption of various adsorbates with different properties on the MgO(100) surface show that the overall performance of QM/MM with the LSEC method is excellent for the adsorption energies, geometries, and charge properties. The excellent performance holds for both the nonstoichiometric and stoichiometric clusters (i.e., Mg9O14 and Mg9O9, respectively), demonstrating the robustness of the LSEC method. We expect that the LSEC method can be combined with QM/MM or used separately for future chemical studies of metal oxides and other ionically bonded systems.

Unveiling the mechanisms of carboxylic acid esterification on acid zeolites for biomass-to-energy: A review of the catalytic process through experimental and computational studies

In recent years, there has been significant interest from industrial and academic areas in the esterification of carboxylic acids catalyzed by acidic zeolites, as it represents a sustainable and economically viable approach to producing a wide range of high-value-added products. However, there is a lack of comprehensive reviews that address the intricate reaction mechanisms occurring at the catalyst interface at both the experimental and atomistic levels. Therefore, in this review, we provide an overview of the esterification reaction on acidic zeolites based on experimental and theoretical studies. The combination of infrared spectroscopy with atomistic calculations and experimental strategies using modulation excitation spectroscopy techniques combined with phase-sensitive detection is presented as an approach to detecting short-lived intermediates at the interface of zeolitic frameworks under realistic reaction conditions. To achieve this goal, this review has been divided into four sections: The first is a brief introduction highlighting the distinctive features of this review. The second addresses questions about the topology and activity of different zeolitic systems, since these properties are closely correlated in the esterification process. The third section deals with the mechanisms proposed in the literature. The fourth section presents advances in IR techniques and theoretical calculations that can be applied to gain new insights into reaction mechanisms. Finally, this review concludes with a subtle approach, highlighting the main aspects and perspectives of combining experimental and theoretical techniques to elucidate different reaction mechanisms in zeolitic systems.

Nanostructured Design Cathode Materials for Magnesium-Ion Batteries

Energy is undeniably one of the most fundamental requirements of the current generation. Solar and wind energy are sustainable and renewable energy sources; however, their unpredictability points to the development of energy storage systems (ESSs). There has been a substantial increase in the use of batteries, particularly lithium-ion batteries (LIBs), as ESSs. However, low rate capability and degradation due to electric load in long-range electric vehicles are pushing LIBs to their limits. As alternative ESSs, magnesium-ion batteries (MIBs) possess promising properties and advantages. Cathode materials play a crucial role in MIBs. In this regard, a variety of cathode materials, including Mn-based, Se-based, vanadium- and vanadium oxide-based, S-based, and Mg2+-containing cathodes, have been investigated by experimental and theoretical techniques. Results reveal that the discharge capacity, capacity retention, and cycle life of cathode materials need improvement. Nevertheless, maintaining the long-term stability of the electrode-electrolyte interface during high-voltage operation continues to be a hurdle in the execution of MIBs, despite the continuous research in this field. The current Review mainly focuses on the most recent nanostructured-design cathode materials in an attempt to draw attention to MIBs and promote the investigation of suitable cathode materials for this promising energy storage device.

Combined DFT and Kinetic Monte Carlo Study of UiO-66 Catalysts for γ-Valerolactone Production

Zr-based metal-organic frameworks (MOFs) are excellent heterogeneous porous catalysts due to their thermal stability. Their tunability via node and linker modifications makes them amenable for theoretical studies on catalyst design. However, detailed benchmarks on MOF-based reaction mechanisms combined with kinetics analysis are still scarce. Thus, we here evaluate different computational models and density functional theory (DFT) methods followed by kinetic Monte Carlo studies for a case reaction relevant in biomass upgrading, i.e., the conversion of methyl levulinate to γ-valerolactone catalyzed by UiO-66. We show the impact of cluster versus periodic models, the importance of the DF of choice, and the direct comparison to experimental data via simulated kinetics data. Overall, we found that Perdew-Burke-Ernzerhof (PBE), a widely employed method in plane-wave periodic calculations, greatly overestimates reaction rates, while M06 with cluster models better fits the available experimental data and is recommended whenever possible.

Molecular-scale Insights into Cooperativity Switching of xTAB Adsorption on Gold Nanoparticles

Quantifying adsorption behaviors is crucial for various applications such as catalysis, separation, and sensing, yet it is generally challenging to access in solution. Here, we report a combined experimental and computational study of the adsorption behaviors of alkyl-trimethylammonium bromides (xTAB), a class of ligands important for colloidal nanoparticle stabilization and shape control, with various alkyl chain lengths x on Au nanoparticles. We use density functional theory (DFT) to calculate xTAB binding energies on Au{111} and Au{110} surfaces with standing-up and lying-down configurations, which provides insights into the adsorption affinity and cooperativity differences of xTAB on these two facets. We demonstrate the key role of van der Waals interactions in determining the xTAB adsorption behavior. These computational results predict and explain the experimental discovery of xTAB's adsorption behavior switch from stronger affinity, negative cooperativity to weaker affinity, positive cooperativity when the concentration of xTAB increases in solution. We also show that in the standing-up configuration, bilayer adsorption may occur on both facets, which can lead to different differential binding energies and consequently adsorption crossover between the two facets when the ligand concentration increases. Our combined experimental and computational approaches demonstrate a paradigm for gaining molecular-scale insights into adsorbate-surface interactions.

Speeding up the Detection of Adsorbate Lateral Interactions in Graph-Theoretical Kinetic Monte Carlo Simulations

Kinetic Monte Carlo (KMC) has become an indispensable tool in heterogeneous catalyst discovery, but realistic simulations remain computationally demanding on account of the need to capture complex and long-range lateral interactions between adsorbates. The Zacros software package (https://zacros.org) adopts a graph-theoretical cluster expansion (CE) framework that allows such interactions to be computed with a high degree of generality and fidelity. This involves solving a series of subgraph isomorphism problems in order to identify relevant interaction patterns in the lattice. In an effort to reduce the computational burden, we have adapted two well-known subgraph isomorphism algorithms, namely, VF2 and RI, for use in KMC simulations and implemented them in Zacros. To benchmark their performance, we simulate a previously established model of catalytic NO oxidation, treating the O* lateral interactions with a series of progressively larger CEs. For CEs with long-range interactions, VF2 and RI are found to provide impressive speedups relative to simpler algorithms. RI performs best, giving speedups reaching more than 150× when combined with OpenMP parallelization. We also simulate a recently developed methane cracking model, showing that RI offers significant improvements in performance at high surface coverages.

Benchmarking the Accuracy of Density Functional Theory against the Random Phase Approximation for the Ethane Dehydrogenation Network on Pt(111)

The Random Phase Approximation (RPA) is conceptually the most accurate Density Functional Approximation method, able to simultaneously predict both adsorbate and surface energies accurately; however, this work questions its superiority over DFT for catalytic application on hydrocarbon systems. This work uses microkinetic modeling to benchmark the accuracy of DFT functionals against that of RPA for the ethane dehydrogenation reaction on Pt(111). Eight different functionals, with and without dispersion corrections, across the GGA, meta-GGA and hybrid classes are evaluated: PBE, PBE-D3, RPBE, RPBE-D3, BEEF-vdW, SCAN, SCAN-rVV10, and HSE06. We show that PBE and RPBE, without dispersion correction, closely model RPA energies for adsorption, transition states, reaction, and activation energies. Next, RPA fails to describe the gas phase energy as unsaturation and chain-length increases in the hydrocarbon. Finally, we show that RPBE has the best accuracy-to-cost ratio, and RPA is likely not superior to RPBE or BEEF-vdW, which also gives a measure of uncertainty.

Accurate Force Fields for Atomistic Simulations of Oxides, Hydroxides, and Organic Hybrid Materials up to the Micrometer Scale

The simulation of metals, oxides, and hydroxides can accelerate the design of therapeutics, alloys, catalysts, cement-based materials, ceramics, bioinspired composites, and glasses. Here we introduce the INTERFACE force field (IFF) and surface models for α-Al2O3, α-Cr2O3, α-Fe2O3, NiO, CaO, MgO, β-Ca(OH)2, β-Mg(OH)2, and β-Ni(OH)2. The force field parameters are nonbonded, including atomic charges for Coulomb interactions, Lennard-Jones (LJ) potentials for van der Waals interactions with 12-6 and 9-6 options, and harmonic bond stretching for hydroxide ions. The models outperform DFT calculations and earlier atomistic models (Pedone, ReaxFF, UFF, CLAYFF) up to 2 orders of magnitude in reliability, compatibility, and interpretability due to a quantitative representation of chemical bonding consistent with other compounds across the periodic table and curated experimental data for validation. The IFF models exhibit average deviations of 0.2% in lattice parameters, <10% in surface energies (to the extent known), and 6% in bulk moduli relative to experiments. The parameters and models can be used with existing parameters for solvents, inorganic compounds, organic compounds, biomolecules, and polymers in IFF, CHARMM, CVFF, AMBER, OPLS-AA, PCFF, and COMPASS, to simulate bulk oxides, hydroxides, electrolyte interfaces, and multiphase, biological, and organic hybrid materials at length scales from atoms to micrometers. The nonbonded character of the models also enables the analysis of mixed oxides, glasses, and certain chemical reactions, and well-performing nonbonded models for silica phases, SiO2, are introduced. Automated model building is available in the CHARMM-GUI Nanomaterial Modeler. We illustrate applications of the models to predict the structure of mixed oxides, and energy barriers of ion migration, as well as binding energies of water and organic molecules in outstanding agreement with experimental data and calculations at the CCSD(T) level. Examples of model building for hydrated, pH-sensitive oxide surfaces to simulate solid-electrolyte interfaces are discussed.

Intermetallic Nanocrystals for Fuel-Cells-Based Electrocatalysis

Electrocatalysis underpins the renewable electrochemical conversions for sustainability, which further replies on metallic nanocrystals as vital electrocatalysts. Intermetallic nanocrystals have been known to show distinct properties compared to their disordered counterparts, and been long explored for functional improvements. Tremendous progresses have been made in the past few years, with notable trend of more precise engineering down to an atomic level and the investigation transferring into more practical membrane electrode assembly (MEA), which motivates this timely review. After addressing the basic thermodynamic and kinetic fundamentals, we discuss classic and latest synthetic strategies that enable not only the formation of intermetallic phase but also the rational control of other catalysis-determinant structural parameters, such as size and morphology. We also demonstrate the emerging intermetallic nanomaterials for potentially further advancement in energy electrocatalysis. Then, we discuss the state-of-the-art characterizations and representative intermetallic electrocatalysts with emphasis on oxygen reduction reaction evaluated in a MEA setup. We summarize this review by laying out existing challenges and offering perspective on future research directions toward practicing intermetallic electrocatalysts for energy conversions.

Many-Body Methods for Surface Chemistry Come of Age: Achieving Consensus with Experiments

The adsorption energy of a molecule onto the surface of a material underpins a wide array of applications, spanning heterogeneous catalysis, gas storage, and many more. It is the key quantity where experimental measurements and theoretical calculations meet, with agreement being necessary for reliable predictions of chemical reaction rates and mechanisms. The prototypical molecule-surface system is CO adsorbed on MgO, but despite intense scrutiny from theory and experiment, there is still no consensus on its adsorption energy. In particular, the large cost of accurate many-body methods makes reaching converged theoretical estimates difficult, generating a wide range of values. In this work, we address this challenge, leveraging the latest advances in diffusion Monte Carlo (DMC) and coupled cluster with single, double, and perturbative triple excitations [CCSD(T)] to obtain accurate predictions for CO on MgO. These reliable theoretical estimates allow us to evaluate the inconsistencies in published temperature-programed desorption experiments, revealing that they arise from variations in employed pre-exponential factors. Utilizing this insight, we derive new experimental estimates of the (electronic) adsorption energy with a (more) precise pre-exponential factor. As a culmination of all of this effort, we are able to reach a consensus between multiple theoretical calculations and multiple experiments for the first time. In addition, we show that our recently developed cluster-based CCSD(T) approach provides a low-cost route toward achieving accurate adsorption energies. This sets the stage for affordable and reliable theoretical predictions of chemical reactions on surfaces to guide the realization of new catalysts and gas storage materials.

N,S coordination in Ni single-atom catalyst promoting CO2RR towards HCOOH

Carbon-based single atom catalysts (SACs) are attracting extensive attention in the CO2 reduction reaction (CO2RR) due to their maximal atomic utilization, easily regulated active center and high catalytic activity, in which the coordination environment plays a crucial role in the intrinsic catalytic activity. Taking NiN4 as an example, this study reveals that the introduction of different numbers of S atoms into N coordination (Ni-NxS4-x (x = 1-4)) results in outstanding structural stability and catalytic activity. Owing to the additional orbitals around -1.60 eV and abundant Ni dxz, dyz, dx2, and dz2 orbital occupation after S substitution, N,S coordination can effectively facilitate the protonation of adsorbed intermediates and thus accelerate the overall CO2RR. The CO2RR mechanisms for CO and HCOOH generation via two-electron pathways are systematically elucidated on NiN4, NiN3S1 and NiN2S2. NiN2S2 yields HCOOH as the most favorable product with a limiting potential of -0.24 V, surpassing NiN4 (-1.14 V) and NiN3S1 (-0.50 V), which indicates that the different S-atom substitution of NiN4 has considerable influence on the CO2RR performance. This work highlights NiN2S2 as a high-performance CO2RR catalyst to produce HCOOH, and demonstrates that N,S coordination is an effective strategy to regulate the performance of atomically dispersed electrocatalysts.

Tailoring Ag Electron Donating Ability for Organohalide Reduction: A Bilayer Electrode Design

Electrochemical reduction of organohalides provides a green approach in the reduction of environmental pollutants, the synthesis of new organic molecules, and many other applications. The presence of a catalytic electrode can make the process more energetically efficient. Ag is known to be a very good electrode for the reduction of a wide range of organohalides. Herein, we examine the elementary adsorption and reaction steps that occur on Ag and the changes that result from changes in the Ag-coated metal, strain in Ag, solvent, and substrate geometry. The results are used to develop an electrode design strategy that can possibly be used to further increase the catalytic activity of pure Ag electrodes. We have shown how epitaxially depositing one to three layers of Ag on catalytically inert or less active support metal can increase the surface electron donating ability, thus increasing the adsorption of organic halide and the catalytic activity. Many factors, such as molecular geometry, lattice mismatches, work function, and solvents, contribute to the adsorption of organic halide molecules over the bilayer electrode surface. To isolate and rank these factors, we examined three model organic halides, namely, halothane, bromobenzene (BrBz), and benzyl bromide (BzBr) adsorption on Ag/metal (metal = Au, Bi, Pt, and Ti) bilayer electrodes in both vacuum and acetonitrile (ACN) solvent. The different metal supports offer a range of lattice mismatches and work function differences with Ag. Our calculations show that the surface of Ag becomes more electron donating and accessible to adsorption when it forms a bilayer with Ti as it has a lower work function and almost zero lattice mismatch with Ag. We believe this study will help to increase the electron donating ability of the Ag surface by choosing the right metal support, which in turn can improve the catalytic activity of the working electrode.

Automated, Consistent, and Even-Handed Selection of Active Orbital Spaces for Quantum Embedding

A widely used strategy to reduce the computational cost of quantum-chemical calculations is to partition the system into an active subsystem, which is the focus of the computational efforts, and an environment that is treated at a lower computational level. The system partitioning is mostly based on localized molecular orbitals. When reaction paths or energy differences are to be calculated, it is crucial to keep the orbital space consistent for all structures. Inconsistencies in orbital space can lead to unpredictable errors on the potential energy surface. While successful strategies to ensure this consistency have been established for organic and even metal-organic systems, these methods often fail for metal clusters or nanoparticles with a high density of near-degenerate and delocalized molecular orbitals. However, such systems are highly relevant for catalysis. Accurate yet feasible quantum-mechanical ab initio calculations are therefore highly desired. In this work, we present an approach based on the subsystem projected atomic orbital decomposition algorithm that allows us to ensure automated and consistent partitioning even for systems with delocalized and near-degenerate molecular orbitals and demonstrate the validity of this method for the binding energies of small molecules on transition-metal clusters.

Data-Driven Prediction of Configurational Stability of Molecule-Adsorbed Heterogeneous Catalysts

The design of new heterogeneous catalysts that convert small molecules into valuable chemicals is a key challenge for constructing sustainable energy systems. Density functional theory (DFT)-based design frameworks based on the understanding of molecular adsorption on the catalytic surface have been widely proposed to accelerate experimental approaches to develop novel catalysts. In addition, a machine learning (ML)-combined design framework was recently proposed to further reduce the inherent time cost of DFT-based frameworks. However, because of the lack of prior information on chemical interactions between arbitrary surfaces and adsorbates, the efficacy of the computational screening approaches would be reduced by obtaining unexpected structural anomalies (i.e., abnormally converged surface-adsorbate geometries after the DFT calculations) during an exhaustive exploration of chemical space. To overcome this challenge, we propose an ML framework that directly predicts the configurational stability of a given initial surface-adsorbate geometry. Our benchmark experiments with the Open Catalysts 20 (OC20) dataset show promising performance on classifying stable geometry (i.e., F1-score of 0.922, the area under the receiver operating characteristics (AUROC) of 0.906, and Matthews correlation coefficient (MCC) of 0.633) with a high precision of 0.921 by utilizing an ensemble approach. We further interpret the generalizability and domain applicability of the trained model in terms of the chemical space of the OC20 dataset. Furthermore, from an experiment on the training set size dependence of model performance, we found that our ML model could be practically applicable to classify stable configurations even with a relatively small number of training data.

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.

Correlation Consistent Basis Sets for Explicitly Correlated Theory: The Transition Metals

We present correlation consistent basis sets for explicitly correlated (F12) calculations, denoted VnZ(-PP)-F12-wis (n = D,T), for the d-block elements. The cc-pVDZ-F12-wis basis set is contracted to [8s7p5d2f] for the 3d-block, while its ECP counterpart for the 4d and 5d-blocks, cc-pVDZ-PP-F12-wis, is contracted to [6s6p5d2f]. The corresponding contracted sizes for cc-pVTZ(-PP)-F12-wis are [9s8p6d3f2g] for the 3d-block elements and [7s7p6d3f2g] for the 4d and 5d-block elements. Our VnZ(-PP)-F12-wis basis sets are evaluated on challenging test sets for metal-organic barrier heights (MOBH35) and group-11 metal clusters (CUAGAU-2). In F12 calculations, they are found to be about as close to the complete basis set limit as the combination of standard cc-pVnZ-F12 on main-group elements with the standard aug-cc-pV(n+1)Z(-PP) basis sets on the transition metal(s). While our basis sets are somewhat more compact than aug-cc-pV(n+1)Z(-PP), the CPU time benefit is negligible for catalytic complexes that contain only one or two transition metals among dozens of main-group elements; however, it is somewhat more significant for metal clusters.

Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations

Within this Perspective, we critically reflect on the role of first-principles molecular dynamics (MD) simulations in unraveling the catalytic function within zeolites under operating conditions. First-principles MD simulations refer to methods where the dynamics of the nuclei is followed in time by integrating the Newtonian equations of motion on a potential energy surface that is determined by solving the quantum-mechanical many-body problem for the electrons. Catalytic solids used in industrial applications show an intriguing high degree of complexity, with phenomena taking place at a broad range of length and time scales. Additionally, the state and function of a catalyst critically depend on the operating conditions, such as temperature, moisture, presence of water, etc. Herein we show by means of a series of exemplary cases how first-principles MD simulations are instrumental to unravel the catalyst complexity at the molecular scale. Examples show how the nature of reactive species at higher catalytic temperatures may drastically change compared to species at lower temperatures and how the nature of active sites may dynamically change upon exposure to water. To simulate rare events, first-principles MD simulations need to be used in combination with enhanced sampling techniques to efficiently sample low-probability regions of phase space. Using these techniques, it is shown how competitive pathways at operating conditions can be discovered and how broad transition state regions can be explored. Interestingly, such simulations can also be used to study hindered diffusion under operating conditions. The cases shown clearly illustrate how first-principles MD simulations reveal insights into the catalytic function at operating conditions, which could not be discovered using static or local approaches where only a few points are considered on the potential energy surface (PES). Despite these advantages, some major hurdles still exist to fully integrate first-principles MD methods in a standard computational catalytic workflow or to use the output of MD simulations as input for multiple length/time scale methods that aim to bridge to the reactor scale. First of all, methods are needed that allow us to evaluate the interatomic forces with quantum-mechanical accuracy, albeit at a much lower computational cost compared to currently used density functional theory (DFT) methods. The use of DFT limits the currently attainable length/time scales to hundreds of picoseconds and a few nanometers, which are much smaller than realistic catalyst particle dimensions and time scales encountered in the catalysis process. One solution could be to construct machine learning potentials (MLPs), where a numerical potential is derived from underlying quantum-mechanical data, which could be used in subsequent MD simulations. As such, much longer length and time scales could be reached; however, quite some research is still necessary to construct MLPs for the complex systems encountered in industrially used catalysts. Second, most currently used enhanced sampling techniques in catalysis make use of collective variables (CVs), which are mostly determined based on chemical intuition. To explore complex reactive networks with MD simulations, methods are needed that allow the automatic discovery of CVs or methods that do not rely on a priori definition of CVs. Recently, various data-driven methods have been proposed, which could be explored for complex catalytic systems. Lastly, first-principles MD methods are currently mostly used to investigate local reactive events. We hope that with the rise of data-driven methods and more efficient methods to describe the PES, first-principles MD methods will in the future also be able to describe longer length/time scale processes in catalysis. This might lead to a consistent dynamic description of all steps-diffusion, adsorption, and reaction-as they take place at the catalyst particle level.

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

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

Adsorbate-Induced Adatom Formation on Lithium, Iron, Cobalt, Ruthenium, and Rhenium Surfaces

Recent experimental and theoretical studies have demonstrated the reaction-driven metal-metal bond breaking in metal catalytic surfaces even under relatively mild conditions. Here, we construct a density functional theory (DFT) database for the adsorbate-induced adatom formation energy on the close-packed facets of three hexagonal close-packed metals (Co, Ru, and Re) and two body-centered cubic metals (Li and Fe), where the source of the ejected metal atom is either a step edge or a close-packed surface. For Co and Ru, we also considered their metastable face-centered cubic structures. We studied 18 different adsorbates relevant to catalytic processes and predicted noticeably easier adatom formation on Li and Fe compared to the other three metals. The NH3- and CO-induced adatom formation on Fe(110) is possible at room temperature, a result relevant to NH3 synthesis and Fischer-Tropsch synthesis, respectively. There also exist other systems with favorable adsorbate effects for adatom formation relevant to catalytic processes at elevated temperatures (500-700 K). Our results offer insight into the reaction-driven formation of metal clusters, which could play the role of active sites in reactions catalyzed by Li, Fe, Co, Ru, and Re catalysts.

Electron delocalization of robust high-nuclear bismuth-oxo clusters for promoted CO2 electroreduction

The integration of high activity, selectivity and stability in one electrocatalyst is highly desirable for electrochemical CO2 reduction (ECR), yet it is still a knotty issue. The unique electronic properties of high-nuclear clusters may bring about extraordinary catalytic performance; however, construction of a high-nuclear structure for ECR remains a challenging task. In this work, a family of calix[8]arene-protected bismuth-oxo clusters (BiOCs), including Bi4 (BiOC-1/2), Bi8Al (BiOC-3), Bi20 (BiOC-4), Bi24 (BiOC-5) and Bi40Mo2 (BiOC-6), were prepared and used as robust and efficient ECR catalysts. The Bi40Mo2 cluster in BiOC-6 is the largest metal-oxo cluster encapsulated by calix[8]arenes. As an electrocatalyst, BiOC-5 exhibited outstanding electrochemical stability and 97% Faraday efficiency for formate production at a low potential of -0.95 V vs. RHE, together with a high turnover frequency of up to 405.7 h-1. Theoretical calculations reveal that large-scale electron delocalization of BiOCs is achieved, which promotes structural stability and effectively decreases the energy barrier of rate-determining *OCHO generation. This work provides a new perspective for the design of stable high-nuclear clusters for efficient electrocatalytic CO2 conversion.

Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials

Realistically modelling how solvents affect catalytic reactions is a longstanding challenge due to its prohibitive computational cost. Typically, an explicit atomistic treatment of the solvent molecules is needed together with molecular dynamics (MD) simulations and enhanced sampling methods. Here, we demonstrate the utility of machine learning interatomic potentials (MLIPs), coupled with active learning, to enable fast and accurate explicit solvent modelling of adsorption and reactions on heterogeneous catalysts. MLIPs trained on-the-fly were able to accelerate ab initio MD simulations by up to 4 orders of magnitude while reproducing with high fidelity the geometrical features of water in the bulk and at metal-water interfaces. Using these ML-accelerated simulations, we accurately predicted key catalytic quantities such as the adsorption energies of CO*, OH*, COH*, HCO*, and OCCHO* on Cu surfaces and the free energy barriers of C-H scission of ethylene glycol over Cu and Pd surfaces, as validated with ab initio calculations. We envision that such simulations will pave the way towards detailed and realistic studies of solvated catalysts at large time- and length-scales.

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.

Machine learning of atomic dynamics and statistical surface identities in gold nanoparticles

It is known that metal nanoparticles (NPs) may be dynamic and atoms may move within them even at fairly low temperatures. Characterizing such complex dynamics is key for understanding NPs' properties in realistic regimes, but detailed information on, e.g., the stability, survival, and interconversion rates of the atomic environments (AEs) populating them are non-trivial to attain. In this study, we decode the intricate atomic dynamics of metal NPs by using a machine learning approach analyzing high-dimensional data obtained from molecular dynamics simulations. Using different-shape gold NPs as a representative example, an AEs' dictionary allows us to label step-by-step the individual atoms in the NPs, identifying the native and non-native AEs and populating them along the MD simulations at various temperatures. By tracking the emergence, annihilation, lifetime, and dynamic interconversion of the AEs, our approach permits estimating a "statistical equivalent identity" for metal NPs, providing a comprehensive picture of the intrinsic atomic dynamics that shape their properties.

Linking Experimental and Ab Initio Thermochemistry of Adsorbates with a Generalized Thermochemical Hierarchy

Enthalpies of formation of adsorbates are crucial parameters in the microkinetic modeling of heterogeneously catalyzed reactions since they quantify the stability of intermediates on the catalyst surface. This quantity is often computed using density functional theory (DFT), as more accurate methods are computationally still too expensive, which means that the derived enthalpies have a large uncertainty. In this study, we propose a new error cancellation method to compute the enthalpies of formation of adsorbates from DFT more accurately through a generalized connectivity-based hierarchy. The enthalpy of formation is determined through a hypothetical reaction that preserves atomistic and bonding environments. The method is applied to a data set of 60 adsorbates on Pt(111) with up to 4 heavy (non-hydrogen) atoms. Enthalpies of formation of the fragments required for the bond balancing reactions are based on experimental heats of adsorption for Pt(111). The comparison of enthalpies of formation derived from different DFT functionals using the isodesmic reactions shows that the effect of the functional is significantly reduced due to the error cancellation. Thus, the proposed methodology creates an interconnected thermochemical network of adsorbates that combines experimental with ab initio thermochemistry in a single and more accurate thermophysical database.

Research on Carbon Emission of Solar Grade Polysilicon Produced by Metallurgical Route Using Digital Simulation Technology

Under the enormous pressure of carbon reduction, we need to have a clear understanding of the environmental impact of the energy-intensive and high-emission polysilicon industry. With the rapid development of technology, we now have the ability to monitor the inflow and outflow of materials in enterprises, so as to obtain the life cycle inventory required for environmental impact assessment. And solve the problems of large data collection workload and long working cycle encountered in conventional life cycle assessment. By combining digital simulation technology and life cycle assessment, we analyze carbon dioxide (CO2) emission in each production process of 1 kg solar grade polysilicon (SoG-Si) by metallurgical route (MR) in detail. We not only analyze four typical production processes of MR, namely slag refining, hydrometallurgy, directional solidification and electron beam refining. The production process of metallurgical grade silicon is also analyzed. It is obtained that the production of 1 kg SoG-Si by MR will produce 69.77 kg CO2. The contribution analysis shows that the CO2 produced by electron beam refining, metallurgical silicon smelting, secondary directional solidification and primary directional solidification is more significant, reaching 38.47%, 20.88%, 15.84% and 14.50%, respectively. The sensitivity analysis shows that the sensitivity of electric power in the process of electron beam refining, secondary directional solidification, primary directional solidification and metallurgical silicon smelting is significant, reaching 38.47%, 15.77%, 14.45% and 13.81%, respectively. In addition, according to the analysis results, the improvement suggestions to reduce CO2 emission are given.

Assessing the Impact of Adlayer Description Fidelity on Theoretical Predictions of Coking on Ni(111) at Steam Reforming Conditions

Methane steam reforming is an important industrial process for hydrogen production, employing Ni as a low-cost, highly active catalyst, which, however, suffers from coking due to methane cracking. Coking is the accumulation of a stable poison over time, occurring at high temperatures; thus, to a first approximation, it can be treated as a thermodynamic problem. In this work, we developed an Ab initio kinetic Monte Carlo (KMC) model for methane cracking on Ni(111) at steam reforming conditions. The model captures C-H activation kinetics in detail, while graphene sheet formation is described at the level of thermodynamics, to obtain insights into the "terminal (poisoned) state" of graphene/coke within reasonable computational times. We used cluster expansions (CEs) of progressively higher fidelity to systematically assess the influence of effective cluster interactions between adsorbed or covalently bonded C and CH species on the "terminal state" morphology. Moreover, we compared the predictions of KMC models incorporating these CEs into mean-field microkinetic models in a consistent manner. The models show that the "terminal state" changes significantly with the level of fidelity of the CEs. Furthermore, high-fidelity simulations predict C-CH island/rings that are largely disconnected at low temperatures but completely encapsulate the Ni(111) surface at high temperatures.

Site-Averaged Ab Initio Kinetics: Importance Learning for Multistep Reactions on Amorphous Supports

Single-atom centers on amorphous supports include catalysts for polymerization, partial oxidation, metathesis, hydrogenolysis, and more. The disordered environment makes each site different, and the kinetics exponentially magnifies these differences to make ab initio site-averaged kinetics calculations extremely difficult. This work extends the importance learning algorithm for efficient and precise site-averaged kinetics estimates to ab initio calculations and multistep reaction mechanisms. Specifically, we calculate site-averaged proton transfer relaxation rates on an ensemble of cluster models representing Brønsted acid sites on silica-alumina. We include direct and water-assisted proton transfer pathways and simultaneously estimate the water adsorption and activation enthalpies for forward and backward proton transfers. We use density functional theory (DFT) to obtain a site-averaged rate, somewhat like a turnover frequency, for the proton transfer relaxation rate. Finally, we show that importance learning can provide orders-of-magnitude acceleration over standard sampling methods for site-averaged rate calculations in cases where the rate is dominated by a few highly active sites.