Influence of Van der Waals Interactions on the Solvation Energies of Adsorbates at Pt-Based Electrocatalysts

Minimum conditions for accurate modeling of urea production via co-electrolysis

Co-electrolysis of carbon oxides and nitrogen oxides promise to simultaneously help restore the balance of the C and N cycles while producing valuable chemicals such as urea. However, co-electrolysis processes are still largely inefficient and numerous knowledge voids persist. Here, we provide a solid thermodynamic basis for modelling urea production via co-electrolysis. First, we determine the energetics of aqueous urea produced under electrochemical conditions based on experimental data, which enables an accurate assessment of equilibrium potentials and overpotentials. Next, we use density functional theory (DFT) calculations to model various co-electrolysis reactions producing urea. The calculated reaction free energies deviate significantly from experimental values for well-known GGA, meta-GGA and hybrid functionals. These deviations stem from errors in the DFT-calculated energies of molecular reactants and products. In particular, the error for urea is approximately -0.25 ± 0.10 eV. Finally, we show that all these errors introduce large inconsistencies in the calculated free-energy diagrams of urea production via co-electrolysis, such that gas-phase corrections are strongly advised.

The ABC of Generalized Coordination Numbers and Their Use as a Descriptor in Electrocatalysis

The quest for enhanced electrocatalysts can be boosted by descriptor-based analyses. Because adsorption energies are the most common descriptors, electrocatalyst design is largely based on brute-force routines that comb materials databases until an energetic criterion is verified. In this review, it is shown that an alternative is provided by generalized coordination numbers (denoted by CN ¯ $\overline {{\rm{CN}}} $ or GCN), an inexpensive geometric descriptor for strained and unstrained transition metals and some alloys. CN ¯ $\overline {{\rm{CN}}} $ captures trends in adsorption energies on both extended surfaces and nanoparticles and is used to elaborate structure-sensitive electrocatalytic activity plots and selectivity maps. Importantly, CN ¯ $\overline {{\rm{CN}}} $ outlines the geometric configuration of the active sites, thereby enabling an atom-by-atom design, which is not possible using energetic descriptors. Specific examples for various adsorbates (e.g., *OH, *OOH, *CO, and *H), metals (e.g., Pt and Cu), and electrocatalytic reactions (e.g., O₂  reduction, H₂  evolution, CO oxidation, and reduction) are presented, and comparisons are made against other descriptors.

Using micro-solvation and generalized coordination numbers to estimate the solvation energies of adsorbed hydroxyl on metal nanoparticles

Solvent-adsorbate interactions have a great impact on catalytic processes in aqueous systems. Implicit solvent calculations are inexpensive but inaccurate toward hydrogen bonds, while a full incorporation of explicit solvation is computationally demanding. Micro-solvation attempts to break this dilemma by including only those solvent molecules directly interacting with the solute and any nearby interfaces, thereby providing a compromise between accuracy and computational expenses. Here, we show that micro-solvation of *OH and its relation to adsorption sites is largely transferable across late transition metal nanoparticles. Solvation energies for *OH on nanoparticles of Ir, Pd, and Pt range from -0.63 ± 0.04 eV to -0.67 ± 0.12 eV, while those on Au and Ag are -0.75 ± 0.07 eV and -1.01 ± 0.05 eV, respectively. These results enable the use of average solvation corrections for *OH on late transition metal nanostructures.

Trends in oxygenate/hydrocarbon selectivity for electrochemical CO(2) reduction to C2 products

The electrochemical conversion of carbon di-/monoxide into commodity chemicals paves a way towards a sustainable society but it also presents one of the great challenges in catalysis. Herein, we present the trends in selectivity towards specific dicarbon oxygenate/hydrocarbon products from carbon monoxide reduction on transition metal catalysts, with special focus on copper. We unveil the distinctive role of electrolyte pH in tuning the dicarbon oxygenate/hydrocarbon selectivity. The understanding is based on density functional theory calculated energetics and microkinetic modeling. We identify the critical reaction steps determining selectivity and relate their transition state energies to two simple descriptors, the carbon and hydroxide binding strengths. The atomistic insight gained enables us to rationalize a number of experimental observations and provides avenues towards the design of selective electrocatalysts for liquid fuel production from carbon di-/monoxide.

Simulation of Metal-Supported Metal-Nanoislands: A Comparison of DFT Methods

We have evaluated various density functional theory (DFT) methods to simulate geometric, energetic, electronic, and hydrogen adsorption properties of metal-nanoparticles supported on metal surfaces. We used Pt and Pd nanoislands on Au(111) as model systems. The evaluated DFT methods include GGA (PW91, PBE, RPBE, revPBE, and PBESol), GGA with van der Waals (vdW) corrected (PBE-D3), GGA with optimized vdW functionals (revPBE-vdW), meta-GGA (SCAN and MS2), and the machine learning-based method BEEF-vdW. The results show that the various DFT methods yield similar geometric and electronic properties for Pt (or Pd) nanoislands on Au(111). The DFT methods also produce similar relative energetics for small Pt (or Pd) clusters with different conformations on Au(111). The results show that a triatomic cluster of Pt on Au(111) is more stable with a linear conformation. In contrast, a triatomic cluster of Pd is more stable with a triangular conformation. For clusters with four or more atoms, Pt and Pd clusters on Au(111) prefer non-linear conformation. We found that the various DFT methods yield different results only for the adsorption energy of hydrogen.

A multiscale modelling approach to elucidate the mechanism of the oxygen evolution reaction at the hematite-water interface

Photoelectrochemical (PEC) splitting of water to make hydrogen is a promising clean-energy technology. The oxygen evolution reaction (OER) largely determines the energy efficiency in PEC water-splitting. Hematite, which is a cheap and sustainable semiconductor material with excellent chemical properties, a favourable band gap (2.1 eV) and composed of earth abundant elements is a suitable model photoanode material for studying OER. To understand the design of energy efficient anodes, it is highly desirable to have mechanistic insight into OER at an atomistic level which can be directly connected to experimentally measured quantities. We present a multiscale computational model of OER which connects the thermodynamics and kinetics of elementary charge transfer reactions in OER to kinetics of OER at laboratory length and time scales. We couple density functional theory (DFT) and DFT based molecular dynamics (DFT-MD) simulations with solvent effects at an atomistic level with kinetic Monte Carlo (kMC) simulations at a coarse-grained level in our multiscale model. The time and applied bias potential dependent surface coverage, which are experimentally not known, and the O₂ evolution rate during OER at the hematite-water interface are calculated by the multiscale model. Furthermore, the multiscale model demonstrates the effect of explicitly modelling the interaction of water with the electrode surface via direct adsorption.

Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences

Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)-based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction.

Adsorption processes on a Pd monolayer-modified Pt(111) electrode

Specific adsorption of anions is an important aspect in surface electrochemistry for its influence on reaction kinetics in either a promoted or inhibited fashion. Perchloric acid is typically considered as an ideal electrolyte for investigating electrocatalytic reactions due to the lack of specific adsorption of the perchlorate anion on several metal electrodes. In this work, cyclic voltammetry and computational methods are combined to investigate the interfacial processes on a Pd monolayer deposited on Pt(111) single crystal electrode in perchloric acid solution. The “hydrogen region” of this Pd_MLPt(111) surface exhibits two voltammetric peaks: the first “hydrogen peak” at 0.246 V_RHE actually involves the replacement of hydrogen by hydroxyl, and the second “hydrogen peak” H_II at 0.306 V_RHE appears to be the replacement of adsorbed hydroxyl by specific perchlorate adsorption. The two peaks merge into a single peak when a more strongly adsorbed anion, such as sulfate, is involved. Our density functional theory calculations qualitatively support the peak assignment and show that anions generally bind more strongly to the Pd_MLPt(111) surface than to Pt(111).

Specific adsorption of anions is an important aspect in surface electrochemistry for its influence on reaction kinetics in either a promoted or inhibited fashion.

Influence of Van der Waals Interactions on the Solvation Energies of Adsorbates at Pt-Based Electrocatalysts

Solvation can significantly modify the adsorption energy of species at surfaces, thereby influencing the performance of electrocatalysts and liquid‐phase catalysts. Thus, it is important to understand adsorbate solvation at the nanoscale. Here we evaluate the effect of van der Waals (vdW) interactions described by different approaches on the solvation energy of *OH adsorbed on near‐surface alloys (NSAs) of Pt. Our results show that the studied functionals can be divided into two groups, each with rather similar average *OH solvation energies: (1) PBE and PW91; and (2) vdW functionals, RPBE, PBE‐D3 and RPBE‐D3. On average, *OH solvation energies are less negative by ∼0.14 eV in group (2) compared to (1), and the values for a given alloy can be extrapolated from one functional to another within the same group. Depending on the desired level of accuracy, these concrete observations and our tabulated values can be used to rapidly incorporate solvation into models for electrocatalysis and liquid‐phase catalysis.