Adsorption energies on transition metal surfaces: towards an accurate and balanced description

Strong Dependence of Point Defect Properties in Metal Halide Perovskites on Description of van der Waals Interaction

Weaker than ionic and covalent bonding, van der Waals (vdW) interactions can have a significant impact on structure and function of molecules and materials, including stabilities of conformers and phases, chemical reaction pathways, electro-optical response, electron-vibrational dynamics, etc. Metal halide perovskites (MHPs) are widely investigated for their excellent optoelectronic properties, stemming largely from high defect tolerance. Although MHPs are primarily ionic compounds, we demonstrate that vdW interactions contribute ∼5% to the total energy, and that static, dynamics, electronic and optical properties of point defects in MHPs depend significantly on the vdW interaction model used. Focusing on widely studied CsPbBr3 with the common Br vacancy and interstitial defects, we compare the PBE, PBE+D3, PBE+TS, PBE+TS/HI and PBE+MBD-NL models and show that vdW interactions strongly alter the global and local geometric structure, and change the fundamental bandgap, midgap state energies and electron-vibrational coupling. The vdW interaction sensitivity stems from involvement of heavy and highly polarizable chemical elements and the soft MHP structure.

Toward Realistic Models of the Electrocatalytic Oxygen Evolution Reaction

The electrocatalytic oxygen evolution reaction (OER) supplies the protons and electrons needed to transform renewable electricity into chemicals and fuels. However, the OER is kinetically sluggish; it operates at significant rates only when the applied potential far exceeds the reversible voltage. The origin of this overpotential is hidden in a complex mechanism involving multiple electron transfers and chemical bond making/breaking steps. Our desire to improve catalytic performance has then made mechanistic studies of the OER an area of major scientific inquiry, though the complexity of the reaction has made understanding difficult. While historically, mechanistic studies have relied solely on experiment and phenomenological models, over the past twenty years ab initio simulation has been playing an increasingly important role in developing our understanding of the electrocatalytic OER and its reaction mechanisms. In this Review we cover advances in our mechanistic understanding of the OER, organized by increasing complexity in the way through which the OER is modeled. We begin with phenomenological models built using experimental data before reviewing early efforts to incorporate ab initio methods into mechanistic studies. We go on to cover how the assumptions in these early ab initio simulations─no electric field, electrolyte, or explicit kinetics─have been relaxed. Through comparison with experimental literature, we explore the veracity of these different assumptions. We summarize by discussing the most critical open challenges in developing models to understand the mechanisms of the OER.

Reaction barriers at metal surfaces computed using the random phase approximation: Can we beat DFT in the generalized gradient approximation?

Reaction barriers for molecules dissociating on metal surfaces (as relevant to heterogeneous catalysis) are often difficult to predict accurately with density functional theory (DFT). Although the results obtained for several dissociative chemisorption reactions via DFT in the generalized gradient approximation (GGA), in meta-GGA, and for GGA exchange + van der Waals correlation scatter around the true reaction barrier, there is an entire class of dissociative chemisorption reactions for which GGA-type functionals collectively underestimate the reaction barrier. Little is known why GGA-DFT collectively fails in some cases and not in others, and we do not know whether other methods suffer from the same inconsistency. Here, we present barrier heights for dissociative chemisorption reactions obtained from the random phase approximation in the adiabatic-connection fluctuation-dissipation theorem (ACFDT-RPA) and from hybrid functionals with different amounts of exact exchange. By comparing the results obtained for the dissociative chemisorption reaction of H2 on Al(110) (where GGA-DFT collectively underestimates the barrier) and H2 on Cu(111) (where GGA-DFT scatters around the true barrier), we can gauge whether the inconsistent description of the systems persists for hybrid functionals and ACFDT-RPA. We find hybrid functionals to improve the relative description of the two systems, but to fall short of chemical accuracy. ACFDT-RPA improves the results further and leads to chemically accurate barriers for both systems. Together with an analysis of the density of states and the results from selected GGA, meta-GGA, and GGA exchange + van der Waals correlation functionals, these results allow us to discuss possible origins for the inconsistent behavior of GGA-based functionals for molecule-metal reaction barriers.

Assessing Exchange-Correlation Functionals for Heterogeneous Catalysis of Nitrogen Species

Increasing interest in the sustainable synthesis of ammonia, nitrates, and urea has led to an increase in studies of catalytic conversion between nitrogen-containing compounds using heterogeneous catalysts. Density functional theory (DFT) is commonly employed to obtain molecular-scale insight into these reactions, but there have been relatively few assessments of the exchange-correlation functionals that are best suited for heterogeneous catalysis of nitrogen compounds. Here, we assess a range of functionals ranging from the generalized gradient approximation (GGA) to the random phase approximation (RPA) for the formation energies of gas-phase nitrogen species, the lattice constants of representative solids from several common classes of catalysts (metals, oxides, and metal-organic frameworks (MOFs)), and the adsorption energies of a range of nitrogen-containing intermediates on these materials. The results reveal that the choice of exchange-correlation functional and van der Waals correction can have a surprisingly large effect and that increasing the level of theory does not always improve the accuracy for nitrogen-containing compounds. This suggests that the selection of functionals should be carefully evaluated on the basis of the specific reaction and material being studied.

Thermal degradation of greenhouse gas SF6 at realistic temperatures: Insights from atomic-scale CVHD simulations

Sulfur hexafluoride (SF6), recognized as a potent greenhouse gas with significant contributions to climate change, presents challenges in understanding its degradation processes. Molecular dynamics simulations are valuable tools for understanding modes of decomposition while the traditional approaches face limitations in time scale and require unrealistically high temperatures. The collective variable-driven hyperdynamics (CVHD) approach has been introduced to directly depict the pyrolysis process for SF6 gas at practical application temperatures, as low as 1600 K for the first time. Achieving an unprecedented acceleration factor of up to 107, the method extends the simulation time scale to milliseconds and beyond while maintaining consistency with experimental and theoretical models. The differences in the reaction process between simulations conducted at actual and elevated temperatures have been noted, providing insights into SF6 degradation pathways. The work provides a basis for the further studies on the thermal degradation of pollutants.

Characterization of adsorption sites on IrO2via temperature programmed O2 desorption simulations

This study presents simulations of temperature-programmed desorption (TPD) profiles using desorption energy data from density functional theory (DFT) calculations. We apply this method to investigate the desorption of oxygen (O2) from IrO2(110) to gain insight into the kinetics of oxygen coupling and desorption, important elementary steps in the oxygen evolution reaction (OER). Initially, we confirm the thermodynamically stable adsorption site for oxygen in the pristine IrO2(110) as IrCUS, even with a high oxygen coverage. We successfully simulate TPD for O2 desorption, achieving good agreement with experimental TPD data for different initial oxygen exposures when including more than one adsorption site. We identify a new adsorption site, related to the formation of steps on IrO2(110)(IrCUS-step-0.5), that is essential for reproducing the experimental TPD. Our findings suggest that the observed TPD peaks are the result of different adsorption sites on the surface, rather than solely a lateral interactions effect. This work provides insight into the behavior of oxygen adsorption on IrO2, with implications for understanding surface reactivity and catalytic processes involving this material.

Unsupervised machine learning reveals eigen reactivity of metal surfaces

The reactivity of metal surfaces is a cornerstone concept in chemistry, as metals have long been used as catalysts to accelerate chemical reactions. Although fundamentally important, the reactivity of metal surfaces has hitherto not been explicitly defined. For example, in order to compare the activity of two metal surfaces, a particular probe adsorbate, such as O, H, or CO, has to be specified, as comparisons may vary from probe to probe. Here we report that the metal surfaces actually have their own intrinsic/eigen reactivity, independent of any probe adsorbate. By employing unsupervised machine learning algorithms, specifically, principal component analysis (PCA), two dominant eigenvectors emerged from the binding strength dataset formed by 10 commonly used probes on 48 typical metal surfaces. According to their chemical characteristics revealed by vector decomposition, these two eigenvectors can be defined as the covalent reactivity and the ionic reactivity, respectively. Whereas the ionic reactivity turns out to be related to the work function of the metal surface, the covalent reactivity cannot be indexed by simple physical properties, but appears to be roughly connected with the valence-electron number normalized density of states at the Fermi level. Our findings expose that the metal surface reactivity is essentially a two-dimensional vector rather than a scalar, opening new horizons for understanding interactions at the metal surface.

Influence of nano-BN inclusion and mechanism involved on aluminium-copper alloy

Taking advantage of the high specific surface area of the nanoparticles, boron nitride (BN) nanoparticles were incorporated into the semi-solidified aluminium-copper alloy Al-5Cu-Mn (ZL201) system during the casting process, and its properties and enhancement mechanism were studied. The results shown that the BN in the new composite material is more uniformly distributed in the second phase (Al2Cu), which can promote grain refinement and enhance the bonding with the aluminium-based interface, and the formation of stable phases such as AlB2, AlN, CuN, etc. makes the tensile strength and hardness of the material to be significantly improved (8.5%, 10.2%, respectively). The mechanism of the action of BN in Al2Cu was analyzed by establishing an atomic model and after calculation: BN can undergo strong adsorption on the surface of Al2Cu (0 0 1), and the adsorption energy is lower at the bridge sites on the two cut-off surfaces, which makes the binding of BN to the aluminum base more stable. The charge transfer between B, N and each atom of the matrix can promote the formation of strong covalent bonds Al-N, Cu-N and Al-B bonds, which can increase the dislocation density and hinder the grain boundary slip within the alloy.

Supervised AI and Deep Neural Networks to Evaluate High-Entropy Alloys as Reduction Catalysts in Aqueous Environments

Competitive surface adsorption energies on catalytic surfaces constitute a fundamental aspect of modeling electrochemical reactions in aqueous environments. The conventional approach to this task relies on applying density functional theory, albeit with computationally intensive demands, particularly when dealing with intricate surfaces. In this study, we present a methodological exposition of quantifying competitive relationships within complex systems. Our methodology leverages quantum-mechanical-guided deep neural networks, deployed in the investigation of quinary high-entropy alloys composed of Mo-Cr-Mn-Fe-Co-Ni-Cu-Zn. These alloys are under examination as prospective electrocatalysts, facilitating the electrochemical synthesis of ammonia in aqueous media. Even in the most favorable scenario for nitrogen fixation identified in this study, at the transition from O and OH coverage to surface hydrogenation, the probability of N2 coverage remains low. This underscores the fact that catalyst optimization alone is insufficient for achieving efficient nitrogen reduction. In particular, these insights illuminate that system consideration with oxygen- and hydrogen-repelling approaches or high-pressure solutions would be necessary for improved nitrogen reduction within an aqueous environment.

Hybrid RPA:DFT Approach for Adsorption on Transition Metal Surfaces: Methane and Ethane on Platinum (111)

The hybrid QM:QM approach is extended to adsorption on transition metal surfaces. The random phase approximation (RPA) as the high-level method is applied to cluster models and, using the subtractive scheme, embedded in periodic models which are treated with density functional theory (DFT) that is the low-level method. The PBE functional, both without dispersion and augmented with the many-body dispersion (MBD), is employed. Adsorption of methane and ethane on the Pt(111) surface is studied. For methane in a 2 × 2 surface cell, the hybrid RPA:PBE and RPA:PBE+MBD results, -14.3 and -16.0 kJ mol-1, respectively, are in close agreement with the periodic RPA value of -13.8 kJ mol-1 at significantly reduced computational cost (factor of ∼50). For methane and ethane, the RPA:PBE results (-14.3 and -17.8 kJ mol-1, respectively) indicate underbinding relative to energies derived from experimental desorption barriers for relevant loadings (-15.6 ± 1.6 and -27.2 ± 2.9 kJ mol-1, respectively), whereas the hybrid RPA:PBE+MBD results (-16.0 and -24.9 kJ mol-1, respectively) agree with the experiment well within experimental uncertainty limits (deviation of -0.4 ± 1.5 and +2.3 ± 2.9 kJ mol-1, respectively). Finding a cluster that adequately and robustly represents the adsorbate at the bulk surface is important for the success of the RPA-based QM:QM scheme for metals.

How Precisely Can Individual Molecules Be Analyzed? A Case Study on Locally Quantifying Forces and Energies Using Scanning Probe Microscopy

Recent advances in scanning probe microscopy methodology have enabled the measurement of tip-sample interactions with picometer accuracy in all three spatial dimensions, thereby providing a detailed site-specific and distance-dependent picture of the related properties. This paper explores the degree of detail and accuracy that can be achieved in locally quantifying probe-molecule interaction forces and energies for adsorbed molecules. Toward this end, cobalt phthalocyanine (CoPc), a promising CO2 reduction catalyst, was studied on Ag(111) as a model system using low-temperature, ultrahigh vacuum noncontact atomic force microscopy. Data were recorded as a function of distance from the surface, from which detailed three-dimensional maps of the molecule's interaction with the tip for normal and lateral forces as well as the tip-molecule interaction potential were constructed. The data were collected with a CO molecule at the tip apex, which enabled a detailed visualization of the atomic structure. Determination of the tip-substrate interaction as a function of distance allowed isolation of the molecule-tip interactions; when analyzing these in terms of a Lennard-Jones-type potential, the atomically resolved equilibrium interaction energies between the CO tethered to the tip and the CoPc molecule could be recovered. Interaction energies peaked at less than 160 meV, indicating a physisorption interaction. As expected, the interaction was weakest at the aromatic hydrogens around the periphery of the molecule and strongest surrounding the metal center. The interaction, however, did not peak directly above the Co atom but rather in pockets surrounding it.

Best-of-Both-Worlds Predictive Approach to Dissociative Chemisorption on Metals

Predictive capability, accuracy, and affordability are essential features of a theory that is capable of describing dissociative chemisorption on a metal surface. This type of reaction is important for heterogeneous catalysis. Here we present an approach in which we use diffusion Monte Carlo (DMC) to pin the minimum barrier height and construct a density functional that reproduces this value. This predictive approach allows the construction of a potential energy surface at the cost of density functional theory while retaining near DMC accuracy. Scrutinizing effects of energy dissipation and quantum tunneling, dynamics calculations suggest the approach to be of near chemical accuracy, reproducing molecular beam sticking experiments for the showcase H2 + Al(110) system to ∼1.4 kcal/mol.

Underlying mechanisms of gold nanoalloys stabilization

Gold nanoclusters have attracted significant attention due to their unique physical-chemical properties, which can be tuned by alloying with elements such as Cu, Pd, Ag, and Pt to design materials for various applications. Although Au-nanoalloys have promising applications, our atomistic understanding of the descriptors that drive their stability is far from satisfactory. To address this problem, we considered 55-atom model nanoalloys that have been synthesized by experimental techniques. Here, we combined data mining techniques for creating a large sample of representative configurations, density functional theory for performing total energy optimizations, and Spearman correlation analyses to identify the most important descriptors. Among our results, we have identified trends in core-shell formation in the AuCu and AuPd systems and an onion-like design in the AuAg system, characterized by the aggregation of gold atoms on nanocluster surfaces. These features are explained by Au's surface energy, packing efficiency, and charge transfer mechanisms, which are enhanced by the alloys' preference for adopting the structure of the alloying metal rather than the low-symmetry one presented by Au55. These generalizations provide insights into the interplay between electronic and structural properties in gold nanoalloys, contributing to the understanding of their stabilization mechanisms and potential applications in various fields.

Constructing Mixed Density Functionals for Describing Dissociative Chemisorption on Metal Surfaces: Basic Principles

The production of a majority of chemicals involves heterogeneous catalysis at some stage, and the rates of many heterogeneously catalyzed processes are governed by transition states for dissociative chemisorption on metals. Accurate values of barrier heights for dissociative chemisorption on metals are therefore important to benchmarking electronic structure theory in general and density functionals in particular. Such accurate barriers can be obtained using the semiempirical specific reaction parameter (SRP) approach to density functional theory. However, this approach has thus far been rather ad hoc in its choice of the generic expression of the SRP functional to be used, and there is a need for better heuristic approaches to determining the mixing parameters contained in such expressions. Here we address these two issues. We investigate the ability of several mixed, parametrized density functional expressions combining exchange at the generalized gradient approximation (GGA) level with either GGA or nonlocal correlation to reproduce barrier heights for dissociative chemisorption on metal surfaces. For this, seven expressions of such mixed density functionals are tested on a database consisting of results for 16 systems taken from a recently published slightly larger database called SBH17. Three expressions are derived that exhibit high tunability and use correlation functionals that are either of the PBE GGA form or of one of two limiting nonlocal forms also describing the attractive van der Waals interaction in an approximate way. We also find that, for mixed density functionals incorporating GGA correlation, the optimum fraction of repulsive RPBE GGA exchange obtained with a specific GGA density functional is correlated with the charge-transfer parameter, which is equal to the difference in the work function of the metal surface and the electron affinity of the molecule. However, the correlation is generally not large and not large enough to obtain accurate guesses of the mixing parameter for the systems considered, suggesting that it does not give rise to a very effective search strategy.

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.

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.

Toward the Rational Design of More Efficient Mo2C Catalysts for Hydrodeoxygenation-Mechanism and Descriptor Identification

Viable alternatives to scarce and expensive noble-metal-based catalysts are transition-metal carbides such as Mo and W carbides. It has been shown that these are active and selective catalysts in the hydrodeoxygenation of renewable lipid-based feedstocks. However, the reaction mechanism and the structure-activity relationship of these transition-metal carbides have not yet been fully clarified. In this work, the reaction mechanism of butyric acid hydrodeoxygenation (HDO) over molybdenum carbide (Mo2C) has been studied comprehensively by means of density functional theory coupled with microkinetic modeling. We identified the rate-determining step to be butanol dissociation: C4H9*OH + * → C4H9* + *OH. Then we further explored the possibility to facilitate this step upon heteroatom doping and found that Zr- and Nb-doped Mo2C are the most promising catalysts with enhanced HDO catalytic activity. Linear-scaling relationships were established between the electronic and geometrical descriptors of the dopants and the catalytic performance of various doped Mo2C catalysts. It was demonstrated that descriptors such as dopants' d-band filling and atomic radius play key roles in governing the catalytic activity. This fundamental understanding delivers practical strategies for the rational design of Mo2C-based transition-metal carbide catalysts with improved HDO performance.

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.

Simulating Highly Activated Sticking of H2 on Al(110): Quantum versus Quasi-Classical Dynamics

We evaluate the importance of quantum effects on the sticking of H₂ on Al(110) for conditions that are close to those of molecular beam experiments that have been done on this system. Calculations with the quasi-classical trajectory (QCT) method and with quantum dynamics (QD) are performed using a model in which only motion in the six molecular degrees of freedom is allowed. The potential energy surface used has a minimum barrier height close to the value recently obtained with the quantum Monte Carlo method. Monte Carlo averaging over the initial rovibrational states allowed the QD calculations to be done with an order of magnitude smaller computational expense. The sticking probability curve computed with QD is shifted to lower energies relative to the QCT curve by 0.21 to 0.05 kcal/mol, with the highest shift obtained for the lowest incidence energy. Quantum effects are therefore expected to play a small role in calculations that would evaluate the accuracy of electronic structure methods for determining the minimum barrier height to dissociative chemisorption for H₂ + Al(110) on the basis of the standard procedure for comparing results of theory with molecular beam experiments.