Computational Methods in Heterogeneous Catalysis

Ga and Zn increase the oxygen affinity of Cu-based catalysts for the CO x hydrogenation according to ab initio atomistic thermodynamics

The direct hydrogenation of CO or CO₂ to methanol, a highly vivid research area in the context of sustainable development, is typically carried out with Cu-based catalysts. Specific elements (so-called promoters) improve the catalytic performance of these systems under a broad range of reaction conditions (from pure CO to pure CO₂). Some of these promoters, such as Ga and Zn, can alloy with Cu and their role remains a matter of debate. In that context, we used periodic DFT calculations on slab models and ab initio thermodynamics to evaluate both metal alloying and surface formation by considering multiple surface facets, different promoter concentrations and spatial distributions as well as adsorption of several species (O*, H*, CO* and ) for different gas phase compositions. Both Ga and Zn form an fcc-alloy with Cu due to the stronger interaction of the promoters with Cu than with themselves. While the Cu-Ga-alloy is more stable than the Cu-Zn-alloy at low promoter concentrations (<25%), further increasing the promoter concentration reverses this trend, due to the unfavoured Ga-Ga-interactions. Under CO₂ hydrogenation conditions, a substantial amount of O* can adsorb onto the alloy surfaces, resulting in partial dealloying and oxidation of the promoters. Therefore, the CO₂ hydrogenation conditions are actually rather oxidising for both Ga and Zn despite the large amount of H₂ present in the feedstock. Thus, the growth of a GaO _x /ZnO _x overlayer is thermodynamically preferred under reaction conditions, enhancing CO₂ adsorption, and this effect is more pronounced for the Cu-Ga-system than for the Cu-Zn-system. In contrast, under CO hydrogenation conditions, fully reduced and alloyed surfaces partially covered with H* and CO* are expected, with mixed CO/CO₂ hydrogenation conditions resulting in a mixture of reduced and oxidised states. This shows that the active atmosphere tunes the preferred state of the catalyst, influencing the catalytic activity and stability, indicating that the still widespread image of a static catalyst under reaction conditions is insufficient to understand the complex interplay of processes taking place on a catalyst surface under reaction conditions, and that dynamic effects must be considered.

Insight into the transient inactivation effect on Au/TiO2 catalyst by in-situ DRIFT and UV–vis spectroscopy

Au catalysts have drawn broad attention for catalytic CO oxidation. However, a molecular-level understanding of the reaction mechanism on a fast time-resolved scale is still lacking. Herein, we apply in situ DRIFTS and UV-Vis spectroscopy to monitor the rapid dynamic changes during CO oxidation over Au/TiO₂. A pronounced transient inactivation effect likely due to a structural change of Au/TiO₂ induced by the reactants (CO and O₂) is observed at the beginning of the reaction. The transient inactivation effect is affected by the ratio of CO and O₂ concentrations. More importantly, during the unstable state, the electronic properties of the Au particles change, as indicated by the shift of the CO stretching vibration. UV-Vis spectroscopy corroborates the structure change of Au/TiO₂ surface induced by the reactants, which leads to a weakening of the Au catalyst’s ability to be oxidized (less O₂ adsorption), resulting in the transient inactivation effect.

A molecular-level understanding of the Au-catalyzed CO oxidation on a fast time-resolved scale is still lacking. Here the authors monitor the rapid dynamic changes during CO oxidation over Au/TiO2 using in situ DRIFTS and UV-Vis spectroscopy, and reveal that the catalyst undergoes a surprising structural change at the beginning of the reaction.

Parametrization of the PM7 Semiempirical Quantum Mechanical Method for Silver Nanoclusters

Semiempirical quantum mechanical methods (SEQMs) are widely used in computational chemistry because of their low computational cost, but their accuracy depends on the quality of the parameters. The neglect of diatomic differential overlap method PM7 is among the few SEQMs that contain parameters for Ag, but the experimental reference data was insufficient to obtain reliable parameters in the original parametrization. In this work, we reparametrize the PM7 parameters for Ag to accurately reproduce the ground-state potential energy surfaces of Ag clusters. Since little experimental data is available, we use reference data obtained from the ab initio method CCSD(T). The resulting parameters significantly reduce the errors in binding energies, energies required to displace clusters along their normal modes, and relative energies of isomers compared to the default PM7 Ag parameters.

Merits and Demerits of ODE Modeling of Physicochemical Systems for Numerical Simulations

In comparison with the first-principles calculations mostly using partial differential equations (PDEs), numerical simulations with modeling by ordinary differential equations (ODEs) are sometimes superior in that they are computationally more economical and that important factors are more easily traced. However, a demerit of ODE modeling is the need of model validation through comparison with experimental data or results of the first-principles calculations. In the present review, examples of ODE modeling are reviewed such as sonochemical reactions inside a cavitation bubble, oriented attachment of nanocrystals, dynamic response of flexoelectric polarization, ultrasound-assisted sintering, and dynamics of a gas parcel in a thermoacoustic engine.

High-Throughput Screening of Bicationic Redox Materials for Chemical Looping Ammonia Synthesis

Ammonia recently has gained increasing attention as a carrier for the efficient and safe usage of hydrogen to further advance the hydrogen economy. However, there is a pressing need to develop new ammonia synthesis techniques to overcome the problem of intense energy consumption associated with the widely used Haber-Bosch process. Chemical looping ammonia synthesis (CLAS) is a promising approach to tackle this problem, but the ideal redox materials to drive these chemical looping processes are yet to be discovered. Here, by mining the well-established MP database, the reaction free energies for CLAS involving 1699 bicationic inorganic redox pairs are screened to comprehensively investigate their potentials as efficient redox materials in four different CLAS schemes. A state-of-the-art machine learning strategy is further deployed to significantly widen the chemical space for discovering the promising redox materials from more than half a million candidates. Most importantly, using the three-step H₂ O-CL as an example, a new metric is introduced to determine bicationic redox pairs that are "cooperatively enhanced" compared to their corresponding monocationic counterparts. It is found that bicationic compounds containing a combination of alkali/alkaline-earth metals and transition metal (TM)/post-TM/metalloid elements are compounds that are particularly promising in this respect.

Platinum nanoplatforms: classic catalysts claiming a prominent role in cancer therapy

Platinum nanoparticles (Pt NPs) have a well-established role as a classic heterogeneous catalyst. Also, Pt has traditionally been employed as a component of organometallic drug formulations for chemotherapy. However, a new role in cancer therapy is emerging thanks to its outstanding catalytic properties, enabling novel approaches that are surveyed in this review. Herein, we critically discuss results already obtained and attempt to ascertain future perspectives for Pt NPs as catalysts able to modify key processes taking place in the tumour microenvironment (TME). In addition, we explore relevant parameters affecting the cytotoxicity, biodistribution and clearance of Pt nanosystems. We also analyze pros and cons in terms of biocompatibility and potential synergies that emerge from combining the catalytic capabilities of Pt with other agents such as co-catalysts, external energy sources (near-infrared light, X-ray, electric currents) and conventional therapies.

Rare-Earth Single-Atom Catalysts: A New Frontier in Photo/Electrocatalysis

Single-atom catalysts (SACs) provide well-defined active sites with 100% atom utilization, and can be prepared using a wide range of support materials. Therefore, they are attracting global attention, especially in the fields of energy conversion and storage. To date, research has focused on transition-metal and precious-metal-based SACs. More recently, rare-earth (RE)-based SACs have emerged as a new frontier in photo/electrocatalysis owing to their unique electronic structure arising from the spin-orbit coupling of the 4f and valence orbitals, unsaturated coordination environment, and unique behavior as charge-transport bridges. However, a systematic review on the role of the RE active sites, catalytic mechanisms, and synthetic methods for RE SACs is lacking. Therefore, in this review, the latest developments in RE SACs having applications in photo/electrocatalysis are summarized and discussed. First, the theoretical advantages of RE SACs for photo/electrocatalysis are briefly introduced, focusing on the roles of the 4f orbitals and coupled energy levels. In addition, the most recent research progress on RE SACs is summarized for several important photo/electrocatalytic reactions and the corresponding catalytic mechanisms are discussed. Further, the synthetic strategies for the production of RE SACs are reported. Finally, challenges for the development of RE SACs are highlighted, along with future research directions and perspectives.

Simple Approximation for the Ideal Reference State of Gases Adsorbed on Solid-State Surfaces

Reference states are useful as models for facilitating calculations of equilibrium constants, and they may also serve as standard states that are convenient for organizing and tabulating thermodynamic data; however, standard state conventions and appropriate reference states for adsorbed species have received less attention than those for pure substances and solutes. Here, we compare seven choices of reference states for calculations of equilibrium constants and transition state theory rate constants for flat surfaces, in particular (1) an ideal 2D harmonic oscillator, (2) an ideal rigid-molecule harmonic oscillator, (3) an ideal 2D harmonic oscillator with separable surface modes, (4) a 2D ideal gas, (5) an ideal 2D hindered translator, (6) an ideal 2D hindered translator with lowest-order barriers, and (7) a simple ideal 2D hindered translator proposed in this work. The advantage of models 5-7 is that they can treat both mobile and localized adsorbates in a consistent way, whereas models 1-3 are only appropriate for localized adsorbates, and model 4 is only appropriate for a freely translating adsorbate. Furthermore, models 6 and 7 reduce the computational cost without the user having to calculate barrier heights for diffusion. An advantage of the simple ideal 2D hindered translator is that it has a physical high-temperature limit. We also propose a reference state for nonflat surfaces. The user is encouraged to choose a reference state based on the appropriateness of the model and the practicality of the calculations.

Towards the rational design of Pt-based alloy catalysts for the low-temperature water-gas shift reaction: from extended surfaces to single atom alloys

The rational design of Pt-based catalysts for the low-temperature water-gas-shift (LT-WGS) reaction is an active research field because of its important role played in the fuel cell-based hydrogen economy, especially in mobile applications. Previous theoretical analyses have suggested that Pt alloys, leading to a weaker CO binding affinity than the Pt metal, could help alleviate CO poisoning and thus should be promising catalysts of the LT-WGS reaction. However, experimental research along this line was rather ineffective in the past decade. In the present work, we employed the state-of-the-art kinetic Monte Carlo (KMC) simulations to examine the influences of the electronic effect by introducing sub-surface alloys and/or core–shell structures, and the synergetic effect by introducing single atom alloys on the catalytic performance of Pt-alloy catalysts. Our KMC simulations have highlighted the importance of the OH binding affinity on the catalyst surfaces to reduce the barrier of water dissociation as the rate determining step, instead of the CO binding affinity as has been emphasized before in conventional mean-field kinetic models. Along this new direction of catalyst design, we found that Pt–Ru synergetic effects can significantly increase the activity of the Pt metal, leading to Ru_1–3@Pt alloys with a tetrahedron site of one surface-three subsurface Ru atoms on the Pt host, showing a turnover frequency of about five orders of magnitude higher than the Pt metal.

KMC simulations show that decreasing the barrier of H₂O decomposition is more beneficial than decreasing the CO binding affinity in LT-WGS, while the latter was overemphasized by MF-MKM. Here Ru_1–3@Pt alloy is proposed as a promising catalyst.

Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status, and challenges

Kinetic Monte Carlo (KMC) simulations in combination with first-principles (1p)-based calculations are rapidly becoming the gold-standard computational framework for bridging the gap between the wide range of length scales and time scales over which heterogeneous catalysis unfolds. 1p-KMC simulations provide accurate insights into reactions over surfaces, a vital step toward the rational design of novel catalysts. In this Perspective, we briefly outline basic principles, computational challenges, successful applications, as well as future directions and opportunities of this promising and ever more popular kinetic modeling approach.

Electrocatalysis in Alkaline Media and Alkaline Membrane-Based Energy Technologies

Hydrogen energy-based electrochemical energy conversion technologies offer the promise of enabling a transition of the global energy landscape from fossil fuels to renewable energy. Here, we present a comprehensive review of the fundamentals of electrocatalysis in alkaline media and applications in alkaline-based energy technologies, particularly alkaline fuel cells and water electrolyzers. Anion exchange (alkaline) membrane fuel cells (AEMFCs) enable the use of nonprecious electrocatalysts for the sluggish oxygen reduction reaction (ORR), relative to proton exchange membrane fuel cells (PEMFCs), which require Pt-based electrocatalysts. However, the hydrogen oxidation reaction (HOR) kinetics is significantly slower in alkaline media than in acidic media. Understanding these phenomena requires applying theoretical and experimental methods to unravel molecular-level thermodynamics and kinetics of hydrogen and oxygen electrocatalysis and, particularly, the proton-coupled electron transfer (PCET) process that takes place in a proton-deficient alkaline media. Extensive electrochemical and spectroscopic studies, on single-crystal Pt and metal oxides, have contributed to the development of activity descriptors, as well as the identification of the nature of active sites, and the rate-determining steps of the HOR and ORR. Among these, the structure and reactivity of interfacial water serve as key potential and pH-dependent kinetic factors that are helping elucidate the origins of the HOR and ORR activity differences in acids and bases. Additionally, deliberately modulating and controlling catalyst-support interactions have provided valuable insights for enhancing catalyst accessibility and durability during operation. The design and synthesis of highly conductive and durable alkaline membranes/ionomers have enabled AEMFCs to reach initial performance metrics equal to or higher than those of PEMFCs. We emphasize the importance of using membrane electrode assemblies (MEAs) to integrate the often separately pursued/optimized electrocatalyst/support and membranes/ionomer components. Operando/in situ methods, at multiscales, and ab initio simulations provide a mechanistic understanding of electron, ion, and mass transport at catalyst/ionomer/membrane interfaces and the necessary guidance to achieve fuel cell operation in air over thousands of hours. We hope that this Review will serve as a roadmap for advancing the scientific understanding of the fundamental factors governing electrochemical energy conversion in alkaline media with the ultimate goal of achieving ultralow Pt or precious-metal-free high-performance and durable alkaline fuel cells and related technologies.

Optimizing the Catalytic Activity of Pd-Based Multinary Alloys toward Oxygen Reduction Reaction

The development of cost-effective catalysts for oxygen reduction reaction (ORR) has an enormous impact on fuel cells toward highly efficient low emission energy conversion. Recently, a Pt-free multinary PdAuAgTi alloy was discovered with excellent ORR activity and low overpotential close to that of Pt. To rationalize the experimental results, a model based on first-principles methods accelerated with deep learning is developed to rapidly compute and with high fidelity the *OH adsorption energy on the alloyed surface. The ensemble-average *OH adsorption energy is shown to explain the experimentally reported OER activities of PdAuAgTi and further is utilized to provide precise maps of the catalytic activity in the total composition space. Notably, the ORR activity of PdAuAgTi is found to be optimum in a narrow region of the composition space with 8-12 at. % Ti, which agrees with the experimental finding for enhanced ORR activity at 11-13 at. % Ti. In addition, replacing Au and Ag with the more cost-effective elements Cu and Zn is also shown to yield optimum catalysts for ORR. The current study shows that first-principles methods in conjunction with machine learning approaches are an effective tool for discovering multinary alloy systems for catalytic applications.

Eliminating Delocalization Error to Improve Heterogeneous Catalysis Predictions with Molecular DFT + U

Approximate semilocal density functional theory (DFT) is known to underestimate surface formation energies yet paradoxically overbind adsorbates on catalytic transition-metal oxide surfaces due to delocalization error. The low-cost DFT + U approach only improves surface formation energies for early transition-metal oxides or adsorption energies for late transition-metal oxides. In this work, we demonstrate that this inefficacy arises due to the conventional usage of metal-centered atomic orbitals as projectors within DFT + U. We analyze electron density rearrangement during surface formation and O atom adsorption on rutile transition-metal oxides to highlight that a standard DFT + U correction fails to tune properties when the corresponding density rearrangement is highly delocalized across both metal and oxygen sites. To improve both surface properties simultaneously while retaining the simplicity of a single-site DFT + U correction, we systematically construct multi-atom-centered molecular-orbital-like projectors for DFT + U. We demonstrate this molecular DFT + U approach for tuning adsorption energies and surface formation energies of minimal two-dimensional models of representative early (i.e., TiO₂) and late (i.e., PtO₂) transition-metal oxides. Molecular DFT + U simultaneously corrects adsorption energies and surface formation energies of multilayer models of rutile TiO₂(110) and PtO₂(110) to resolve the paradoxical description of surface stability and surface reactivity of semilocal DFT.

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

Autonomous catalysis research requires elaborate integration of operando experiments into automated workflows. Suitable experimental data for analysis by artificial intelligence can be measured more readily according to standard operating procedures.

Ab initio molecular dynamics with enhanced sampling in heterogeneous catalysis

Enhanced sampling ab initio simulations enable to study chemical phenomena in catalytic systems including thermal effects & anharmonicity, & collective dynamics describing enthalpic & entropic contributions, which can significantly impact on reaction free energy landscapes.

Computational vibrational spectroscopy of molecule-surface interactions: what is still difficult and what can be done about it

Interactions of molecules with solid surfaces are responsible for key functionalities for a range of currently actively pursued technologies, including heterogeneous catalysis for synthesis or decomposition of molecules, sensitization, surface...

A polarizable coarse-grained model for metal, metal oxide and composite metal/metal oxide nanoparticles and its applications

We present a selected set of exemplifying applications of the novel polarizable coarse-grained model to various outstanding problems in the physics and chemistry of nanoparticles.

Photoredox Chemistry with Organic Catalysts: Role of Computational Methods

Organic catalysts have the potential to carry out a wide range of otherwise thermally inaccessible reactions via photoredox routes. Early demonstrated successes of organic photoredox catalysts include one-electron CO₂ reduction and H₂ generation via water splitting. Photoredox systems are challenging to study and design owing to the sheer number and diversity of phenomena involved, including light absorption, emission, intersystem crossing, partial or complete charge transfer, and bond breaking or formation. Designing a viable photoredox route therefore requires consideration of a host of factors such as absorption wavelength, solvent, choice of electron donor or acceptor, and so on. Quantum chemistry methods can play a critical role in demystifying photoredox phenomena. Using one-electron CO₂ reduction with phenylene-based chromophores as an illustrative example, this perspective highlights recent developments in quantum chemistry that can advance our understanding of photoredox processes and proposes a way forward for driving the design and discovery of organic catalysts.

Structure-Reactivity Relationship for Nano-Catalysts in the Hydrogenation/Dehydrogenation Controlled Reaction Systems

For the activity of a nano-catalyst, a general and quantitative solution to building direct structure-reactivity relationship has not yet been established. On top of the first-principle-based kinetic Monte Carlo (KMC) simulations, we developed a model to build the adsorption site dependence of the activity. We applied this model to study the nano effects of Cu catalysts in the water-gas shift reaction. By accumulating the activities of different adsorption sites, our model satisfactorily reproduced the experimental apparent activation energies for catalysts with sizes over hundreds of nanometers, which were out of reach for conventional KMC simulations. Our results disclose that, even for a cubic catalyst with size of 877 nm, its activity can still be closely related to the activity of edge sites, instead of only the exposed Cu(100) facets as might be expected. The present model is expected to be useful for systems that are controlled by the hydrogenation/dehydrogenation processes.

Single-Atom Engineering to Ignite 2D Transition Metal Dichalcogenide Based Catalysis: Fundamentals, Progress, and Beyond

Single-atom catalysis has been recognized as a pivotal milestone in the development history of heterogeneous catalysis by virtue of its superior catalytic performance, ultrahigh atomic utilization, and well-defined structure. Beyond single-atom protrusions, two more motifs of single-atom substitutions and single-atom vacancies along with synergistic single-atom motif assemblies have been progressively developed to enrich the single-atom family. On the other hand, besides traditional carbon material based substrates, a wide variety of 2D transitional metal dichalcogenides (TMDs) have been emerging as a promising platform for single-atom catalysis owing to their diverse elemental compositions, variable crystal structures, flexible electronic structures, and intrinsic activities toward many catalytic reactions. Such substantial expansion of both single-atom motifs and substrates provides an enriched toolbox to further optimize the geometric and electronic structures for pushing the performance limit. Concomitantly, higher requirements have been put forward for synthetic and characterization techniques with related technical bottlenecks being continuously conquered. Furthermore, this burgeoning single-atom catalyst (SAC) system has triggered serial scientific issues about their changeable single atom-2D substrate interaction, ambiguous synergistic effects of various atomic assemblies, as well as dynamic structure-performance correlations, all of which necessitate further clarification and comprehensive summary. In this context, this Review aims to summarize and critically discuss the single-atom engineering development in the whole field of 2D TMD based catalysis covering their evolution history, synthetic methodologies, characterization techniques, catalytic applications, and dynamic structure-performance correlations. In situ characterization techniques are highlighted regarding their critical roles in real-time detection of SAC reconstruction and reaction pathway evolution, thus shedding light on lifetime dynamic structure-performance correlations which lay a solid theoretical foundation for the whole catalytic field, especially for SACs.

Effects of surface diffusion in electrocatalytic CO2 reduction on Cu revealed by kinetic Monte Carlo simulations

Kinetic Monte Carlo (KMC) methods are frequently used for mechanistic studies of thermally driven heterogeneous catalysis systems but are underused for electrocatalysis. Here, we develop a lattice KMC approach for electrocatalytic CO₂ reduction. The work is motivated by a prior experimental report that performed electroreduction of a mixed feed of ¹²CO₂ and ¹³CO on Cu; differences in the ¹³C content of C2 products ethylene and ethanol (Δ¹³C) were interpreted as evidence of site selectivity. The lattice KMC model considers the effect of surface diffusion on this system. In the limit of infinitely fast diffusion (mean-field approximation), the key intermediates ¹²CO* and ¹³CO* would be well mixed on the surface and no evidence of site selectivity could have been observed. Using a simple two-site model and adapting a previously reported microkinetic model, we assess the effects of diffusion on the relative isotope fractions in the products using the estimated surface diffusion rate of CO* from literature reports. We find that the size of the active sites and the total surface adsorbate coverage can have a large influence on the values of Δ¹³C that can be observed. Δ¹³C is less sensitive to the CO* diffusion rate as long as it is within the estimated range. We further offer possible methods to estimate surface distribution of intermediates and to predict intrinsic selectivity of active sites based on experimental observations. This work illustrates the importance of considering surface diffusion in the study of electrochemical CO₂ reduction to multi-carbon products. Our approach is entirely based on a freely available open-source code, so will be readily adaptable to other electrocatalytic systems.

Critical Role of Thermal Fluctuations for CO Binding on Electrocatalytic Metal Surfaces

This work considers the evaluation of density functional theory (DFT) when comparing against experimental observations of CO binding trends on the strong binding Pt(111) and intermediate binding Cu(111) and for weak binding Ag(111) and Au(111) surfaces important in electrocatalysis. By introducing thermal fluctuations using appropriate statistical mechanical NVT and NPT ensembles, we find that the RPBE and B97M-rV DFT functionals yield qualitatively better metal surface strain trends and CO enthalpies of binding for Cu(111) and Pt(111) than found at 0 K, thereby correcting the overbinding by 0.2 to 0.3 eV to yield better agreement with the enthalpies determined from experiment. The importance of dispersion effects are manifest for the weak CO binding Ag(111) and Au(111) surfaces at finite temperatures in which the RPBE functional does not bind CO at all, while the B97M-rV functional shows that the CO-metal interactions are a mixture of chemisorbed and physisorbed species with binding enthalpies that are within ∼0.05 eV of experiment. Across all M(111) surfaces, we show that the B97M-rV functional consistently predicts the correct atop site preference for all metals due to thermally induced surface distortions that preferentially favor the undercoordinated site. This study demonstrates the need to fully account for finite temperature fluctuations to make contact with the binding enthalpies from surface science experiments and electrocatalysis applications.

Gelation and yielding behavior of polymer–nanoparticle hydrogels

Polymer–nanoparticle hydrogels are a unique class of self‐assembled, shear‐thinning, yield‐stress fluids that have demonstrated potential utility in many impactful applications. Here, we present a thorough analysis of the gelation and yielding behavior of these materials with respect to the polymer and nanoparticle component stoichiometry. Through comprehensive rheological and diffusion studies, we reveal insights into the structural dynamics of the polymer nanoparticle network that identify that stoichiometry plays a key role in gelation and yielding, ultimately enabling the development of hydrogel formulations with unique shear‐thinning and yield‐stress behaviors. Access to these materials opens new doors for interesting applications in a variety of fields including tissue engineering, drug delivery, and controlled solution viscosity.

Energy contour exploration with potentiostatic kinematics

We introduce a method of exploring potential energy contours (PECs) in complex dynamical systems based on potentiostatic kinematics wherein the systems are evolved with minimal changes to their potential energy. We construct a simple iterative algorithm for performing potentiostatic kinematics, which uses an estimate curvature to predict new configuration-space coordinates on the PEC and a potentiostat term component to correct for errors in prediction. Our methods are then applied to atomic structure models using an interatomic potential for energy and force evaluations as would commonly be invoked in a molecular dynamics simulation. Using several model systems, we assess the stability and accuracy of the method on different hyperparameters in the implementation of the potentiostatic kinematics. Our implementation is open source and available within the atomic simulation environment package.

MLSolvA: solvation free energy prediction from pairwise atomistic interactions by machine learning

Recent advances in machine learning technologies and their applications have led to the development of diverse structure-property relationship models for crucial chemical properties. The solvation free energy is one of them. Here, we introduce a novel ML-based solvation model, which calculates the solvation energy from pairwise atomistic interactions. The novelty of the proposed model consists of a simple architecture: two encoding functions extract atomic feature vectors from the given chemical structure, while the inner product between the two atomistic feature vectors calculates their interactions. The results of 6239 experimental measurements achieve outstanding performance and transferability for enlarging training data owing to its solvent-non-specific nature. An analysis of the interaction map shows that our model has significant potential for producing group contributions on the solvation energy, which indicates that the model provides not only predictions of target properties but also more detailed physicochemical insights.

1T-MoS2 Coordinated Bimetal Atoms as Active Centers to Facilitate Hydrogen Generation

Anchoring single metal atoms has been demonstrated as an effective strategy to boost the catalytic performance of non-noble metal 1T-MoS₂ towards hydrogen evolution reaction (HER). However, the dual active sites on 1T-MoS₂ still remain a great challenge. Here, first-principles calculations were performed to systematically investigate the electrocatalytic HER activity of single and dual transition metal (TM) atoms bound to the 1T-MoS₂ monolayer (TM@1T-MoS₂). The resulted Ti@1T-MoS₂ exhibits excellent structural stability, near-thermoneutral adsorption of H* and ultralow reaction barrier (0.15 eV). It is a promising single metal atom catalyst for HER, outperformed the reported Co, Ni and Pd anchoring species. Surprisingly, by further introducing Pd atoms coordinated with S atoms or S vacancies on the Ti@1T-MoS₂ surface, the resulted catalyst not only maintains the high HER activity of Ti sites, but also achieves new dual active moiety due to the appropriate H* adsorption free energy on Pd sites. This work is of great significance for realizing dual active centers on 1T-MoS₂ nanosheets and offers new thought for developing high-performance electrocatalysts for HER.

Dressing of Cu Atom over Nickel Cluster Stimulating the Poisoning-Free CO Oxidation: An Ab Initio Study

In this work using first-principles calculations based on spin-polarized density functional theory (DFT), the role of the Cu atom in degrading the poisoning of carbon monoxide (CO) over Ni_nCu clusters is unveiled. The search has been initiated with the examination of structural, magnetic, and electronic properties of Ni_n+1 and Ni_nCu clusters (1 ≤ n ≤ 12). X-ray absorption near-edge structure (XANES) spectra of Ni K-edge are computed to extract the information on the oxidation states and coordination environment of metal sites of the clusters. This study is operated with the two forms of dispersion corrections, i.e., D2 and D3, with standard DFT (with LDA and GGA functionals) for the consideration of van der Waals interactions during CO adsorption. The PBE and PBE-D3 approaches are found to be capable of yielding the experimentally observed preferential site for CO adsorption. The effect of spin-polarization on the reactivity of transition metals (TMs) toward CO adsorption is crucially assessed by the electronic reactivity descriptors such as d-band center, d-band width, and fractional filling of d-band using a spin-polarized d-band center model. The effective charge transfer from Cu to Ni atoms makes Ni atoms more efficient of charge and is attributed to the degrading adsorption of CO over Ni_nCu clusters. The Ni₁₂Cu cluster stands out with good CO oxidation activity for the Langmuir-Hinshelwood (L-H) reaction pathway.

Optimization of High-Entropy Alloy Catalyst for Ammonia Decomposition and Ammonia Synthesis

The successful synthesis of high-entropy alloy (HEA) nanoparticles, a long-sought goal in materials science, opens a new frontier in materials science with applications across catalysis, structural alloys, and energetic materials. Recently, a Co₂₅Mo₄₅Fe₁₀Ni₁₀Cu₁₀ HEA made of earth-abundant elements was shown to have a high catalytic activity for ammonia decomposition, which rivals that of state-of-the-art, but prohibitively expensive, ruthenium catalysts. Using a computational approach based on first-principles calculations in conjunction with data analytics and machine learning, we build a model to rapidly compute the adsorption energy of H, N, and NH_x (x = 1, 2, 3) species on CoMoFeNiCu alloy surfaces with varied alloy compositions and atomic arrangements. We show that the 25/45 Co/Mo ratio identified experimentally as the most active composition for ammonia decomposition increases the likelihood that the surface adsorbs nitrogen equivalently to that of ruthenium while at the same time interacting moderately strongly with intermediates. Our study underscores the importance of computational modeling and machine learning to identify and optimize HEA alloys across their near-infinite materials design space.

Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis

Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.