Enhancing the connection between computation and experiments in electrocatalysis

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.

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.

Harnessing the electronic structure of active metals to lower the overpotential of the electrocatalytic oxygen evolution reaction

Despite substantial advancements in the field of the electrocatalytic oxygen evolution reaction (OER), the efficiency of earth-abundant electrocatalysts remains far from ideal. The difficulty stems from the complex nature of the catalytic system, which limits our fundamental understanding of the process and thus the possibility of a rational improvement of performance. Herein, we shed light on the role played by the tunable 3d configuration of the metal centers in determining the OER catalytic activity by combining electrochemical and spectroscopic measurements with an experimentally validated computational protocol. One-dimensional coordination polymers based on Fe, Co and Ni held together by an oxonato linker were selected as a case study because of their well-defined electronic and geometric structure in the active site, which can be straightforwardly correlated with their catalytic activity. Novel heterobimetallic coordination polymers were also considered, in order to shed light on the cooperativity effects of different metals. Our results demonstrate the fundamental importance of electronic structure effects such as metal spin and oxidation state evolutions along the reaction profile to modulate ligand binding energies and increase catalyst efficiency. We demonstrated that these effects could in principle be exploited to reduce the overpotential of the electrocatalytic OER below its theoretical limit, and we provide basic principles for the development of coordination polymers with a tailored electronic structure and activity.

Ultrathin Rh Nanosheets with Rich Grain Boundaries for Efficient Hydrogen Oxidation Electrocatalysis

Two-dimensional (2D) Pt-group ultrathin nanosheets (NSs) are promising advanced electrocatalysts for energy-related catalytic reactions. However, improving the electrocatalytic activity of 2D Pt-group NSs through the addition of abundant grain boundaries (GBs) and understanding the underlying formation mechanism remain significant challenges. Herein, we report the controllable synthesis of a series of Rh-based nanocrystals (e.g., Rh nanoparticles, Rh NSs, and Rh NSs with GBs) through a CO-mediated kinetic control synthesis route. In light of the 2D NSs' structural advantages and GB modification, the Rh NSs with rich GBs exhibit an enhanced electrocatalytic activity compared to pure Rh NSs and commercial Pt/C toward the hydrogen oxidation reaction (HOR) in alkaline media. Both experimental results and theoretical computations corroborate that the GBs in the Rh NSs have the capacity to ameliorate the adsorption free energy of reaction intermediates during the HOR, thus resulting in outstanding HOR catalytic performance. Our work offers novel perspectives in the realm of developing sophisticated 2D Pt-group metal electrocatalysts with rich GBs for the energy conversion field.

Machine learning-assisted optimization of multi-metal hydroxide electrocatalysts for overall water splitting

Green hydrogen produced via electrochemical water splitting is a suitable candidate to replace emission-intensive fuels. However, the successful widespread adoption of green hydrogen is contingent on the development of low-cost, earth-abundant catalysts. Herein, machine learning models built on experimental data were used to optimize the precursor ratios of hydroxide-based electrocatalysts, with the objective of improving the product's electrocatalytic performance for overall water splitting. The Neural Network-based models were found to be the most effective in predicting and minimizing the overpotentials of the catalysts, reaching a minimum in two iterations. The relatively mild reaction conditions of the synthesis procedure, coupled with its scalability demonstrated herein, renders the optimized catalyst relevant for industrial implementation in the future. The optimized catalyst, characterized to be a molybdate-intercalated CoFe LDH, demonstrated overpotentials of 266 and 272 mV at 10 mA cm-2 for oxygen and hydrogen evolution reactions respectively in alkaline electrolyte, alongside unwavering stability for overall water splitting over 50 h. Overall, our results reflect the efficacy and advantages of machine learning strategies to alleviate the time and labour-intensive nature of experimental optimizations, which can greatly accelerate electrocatalysts research.

Stability of high-entropy alloys under electrocatalytic conditions

High-entropy alloys are claimed to possess superior stability due to thermodynamic contributions. However, this statement mostly lies on a hypothetical basis. In this study, we use on-line inductively coupled plasma mass spectrometer to investigate the dissolution of five representative electrocatalysts in acidic and alkaline media and a wide potential window targeting the most important applications. To address both model and applied systems, we synthesized thin films and carbon-supported nanoparticles ranging from an elemental (Pt) sample to binary (PtRu), ternary (PtRuIr), quaternary (PtRuIrRh), and quinary (PtRuIrRhPd) alloy samples. For certain metals in the high-entropy alloy under alkaline conditions, lower dissolution was observed. Still, the improvement was not striking and can be rather explained by the lowered concentration of elements in the multinary alloys instead of the synergistic effects of thermodynamics. We postulate that this is because of dissolution kinetic effects, which are always present under electrocatalytic conditions, overcompensating thermodynamic contributions.

Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach

Designing novel catalysts is key to solving many energy and environmental challenges. Despite the promise that data science approaches, including machine learning (ML), can accelerate the development of catalysts, truly novel catalysts have rarely been discovered through ML approaches because of one of its most common limitations and criticisms-the assumed inability to extrapolate and identify extraordinary materials. Herein, we demonstrate an extrapolative ML approach to develop new multi-elemental reverse water-gas shift catalysts. Using 45 catalysts as the initial data points and performing 44 cycles of the closed loop discovery system (ML prediction + experiment), we experimentally tested a total of 300 catalysts and identified more than 100 catalysts with superior activity compared to those of the previously reported high-performance catalysts. The composition of the optimal catalyst discovered was Pt(3)/Rb(1)-Ba(1)-Mo(0.6)-Nb(0.2)/TiO2. Notably, niobium (Nb) was not included in the original dataset, and the catalyst composition identified was not predictable even by human experts.

Intrinsic and external active sites of single-atom catalysts

Active components with suitable supports are the common paradigm for industrial catalysis, and the catalytic activity usually increases with minimizing the active component size, generating a new frontier in catalysis, single-atom catalysts (SACs). However, further improvement of SACs activity is limited by the relatively low loading of single atoms (SAs, which are heteroatoms for most SACs, i.e., external active sites) because of the highly favorable aggregation of single heteroatoms during preparation. Research interest should be shifted to investigate SACs with intrinsic SAs, which could circumvent the aggregation of external SAs and consequently increase the SAs loading while maintaining them individual to further improve the activity. In this review, SACs with external or intrinsic SAs are discussed and, at last, the perspectives and challenges for obtaining high-loading SACs with intrinsic SAs are outlined.

Tailoring a local acid-like microenvironment for efficient neutral hydrogen evolution

Electrochemical hydrogen evolution reaction in neutral media is listed as the most difficult challenges of energy catalysis due to the sluggish kinetics. Herein, the Ir-H_xWO₃ catalyst is readily synthesized and exhibits enhanced performance for neutral hydrogen evolution reaction. H_xWO₃ support is functioned as proton sponge to create a local acid-like microenvironment around Ir metal sites by spontaneous injection of protons to WO₃, as evidenced by spectroscopy and electrochemical analysis. Rationalize revitalized lattice-hydrogen species located in the interface are coupled with H_ad atoms on metallic Ir surfaces via thermodynamically favorable Volmer-Tafel steps, and thereby a fast kinetics. Elaborated Ir-H_xWO₃ demonstrates acid-like activity with a low overpotential of 20 mV at 10 mA cm^-2 and low Tafel slope of 28 mV dec^-1, which are even comparable to those in acidic environment. The concept exemplified in this work offer the possibilities for tailoring local reaction microenvironment to regulate catalytic activity and pathway.

Tailoring Metal-Organic Frameworks and Derived Materials for High-Performance Zinc-Air and Alkaline Batteries

Developing multifunctional materials from earth-abundant elements is urgently needed to satisfy the demand for sustainable energy. Herein, we demonstrate a facile approach for the preparation of a metal-organic framework (MOF)-derived Fe₂O₃/C, composited with N-doped reduced graphene oxide (MO-rGO). MO-rGO exhibits excellent bifunctional electrocatalytic activities toward the oxygen evolution reaction (η_j=10 = 273 mV) and the oxygen reduction reaction (half-wave potential = 0.77 V vs reversible hydrogen electrode) with a low ΔE_OER-ORR of 0.88 V in alkaline solutions. A Zn-air battery based on the MO-rGO cathode displays a high specific energy of over 903 W h kg_Zn^-1 (∼290 mW h cm^-2), an excellent power density of 148 mW cm^-2, and an open-circuit voltage of 1.430 V, outperforming the benchmark Pt/C + RuO₂ catalyst. We also hydrothermally synthesized a Ni-MOF that was partially transformed into a Ni-Co-layered double hydroxide (MOF-LDH). A MO-rGO||MOF-LDH alkaline battery exhibits a specific energy of 42.6 W h kg_total mass^-1 (106.5 μW h cm^-2) and an outstanding specific power of 9.8 kW kg_total mass^-1 (24.5 mW cm^-2). This work demonstrates the potential of MOFs and MOF-derived compounds for designing innovative multifunctional materials for catalysis, electrochemical energy storage, and beyond.

Engineering Single-Atom Electrocatalysts for Enhancing Kinetics of Acidic Volmer Reaction

The design of active and low-cost electrocatalyst for hydrogen evolution reaction (HER) is the key to achieving a clean hydrogen energy infrastructure. The most successful design principle of hydrogen electrocatalyst is the activity volcano plot, which is based on Sabatier principle and has been used to understand the exceptional activity of noble metal and design of metal alloy catalysts. However, this application of volcano plot in designing single-atom electrocatalysts (SAEs) on nitrogen doped graphene (TM/N₄C catalysts) for HER has been less successful due to the nonmetallic nature of the single metal atom site. Herein, by performing ab initio molecular dynamics simulations and free energy calculations on a series of SAEs systems (TM/N₄C with TM = 3d, 4d, or 5d metals), we find that the strong charge-dipole interaction between the negatively charged *H intermediate and the interfacial H₂O molecules could alter the transition path of the acidic Volmer reaction and dramatically raise its kinetic barrier, despite its favorable adsorption free energy. Such kinetic hindrance is also experimentally confirmed by electrochemical measurements. By combining the hydrogen adsorption free energy and the physics of competing interfacial interactions, we propose a unifying design principle for engineering the SAEs used for hydrogen energy conversion, which incorporates both thermodynamic and kinetic considerations and allows going beyond the activity volcano model.

Relationship between oxide identity and electrocatalytic activity of platinum for ethanol electrooxidation in perchlorate acidic solution

Water and its dissociated species at the solid‒liquid interface play critical roles in catalytic science; e.g., functions of oxygen species from water dissociation are gradually being recognized. Herein, the relationship between oxide identity (PtOH_ads, PtO_ads, and PtO₂) and electrocatalytic activity of platinum for ethanol electrooxidation was obtained in perchlorate acidic solution over a wide potential range with an upper potential of 1.5 V (reversible hydrogen electrode, RHE). PtOH_ads and α-PtO₂, rather than PtO_ads, act as catalytic centers promoting ethanol electrooxidation. This relationship was corroborated on Pt(111), Pt(110), and Pt(100) electrodes, respectively. A reaction mechanism of ethanol electrooxidation was developed with DFT calculations, in which platinum oxides-mediated dehydrogenation and hydrated reaction intermediate, geminal diol, can perfectly explain experimental results, including pH dependence of product selectivity and more active α-PtO₂ than PtOH_ads. This work can be generalized to the oxidation of other substances on other metal/alloy electrodes in energy conversion and electrochemical syntheses.

pH Effects in a Model Electrocatalytic Reaction Disentangled

Varying the solution pH not only changes the reactant concentrations in bulk solution but also the local reaction environment (LRE) that is shaped furthermore by macroscopic mass transport and microscopic electric double layer (EDL) effects. Understanding ubiquitous pH effects in electrocatalysis requires disentangling these interwoven factors, which is a difficult, if not impossible, task without physical modeling. Herein, we demonstrate how a hierarchical model that integrates microkinetics, double-layer charging, and macroscopic mass transport can help understand pH effects of the formic acid oxidation reaction (FAOR). In terms of the relation between the peak activity and the solution pH, intrinsic pH effects without consideration of changes in the LRE would lead to a bell-shaped curve with a peak at pH = 6. Adding only macroscopic mass transport, we can already reproduce qualitatively the experimentally observed trapezoidal shape with a plateau between pH 5 and 10 in perchlorate and sulfate solutions. A quantitative agreement with experimental data requires consideration of EDL effects beyond Frumkin correlations. Specifically, the peculiar nonmonotonic surface charging relation affects the free energies of adsorbed intermediates. We further discuss pH effects of FAOR in phosphate and chloride-containing solutions, for which anion adsorption becomes important. This study underpins the importance of a full consideration of multiple interrelated factors for the interpretation of pH effects in electrocatalysis.

Cation-Coordinated Inner-Sphere CO2 Electroreduction at Au-Water Interfaces

Electrochemical CO₂ reduction reaction (CO₂RR) is a promising technology for the clean energy economy. Numerous efforts have been devoted to enhancing the mechanistic understanding of CO₂RR from both experimental and theoretical studies. Electrolyte ions are critical for the CO₂RR; however, the role of alkali metal cations is highly controversial, and a complete free energy diagram of CO₂RR at Au-water interfaces is still missing. Here, we provide a systematic mechanism study toward CO₂RR via ab initio molecular dynamics simulations integrated with the slow-growth sampling (SG-AIMD) method. By using the SG-AIMD approach, we demonstrate that CO₂RR is facile at the inner-sphere interface in the presence of K cations, which promote the CO₂ activation with the free energy barrier of only 0.66 eV. Furthermore, the competitive hydrogen evolution reaction (HER) is inhibited by the interfacial cations with the induced kinetic blockage effect, where the rate-limiting Volmer step shows a much higher energy barrier (1.27 eV). Eventually, a comprehensive free energy diagram including both kinetics and thermodynamics of the CO₂RR to CO and the HER at the electrochemical interface is derived, which illustrates the critical role of cations on the overall performance of CO₂ electroreduction by facilitating CO₂ adsorption while suppressing the hydrogen evolution at the same time.

Highly Selective Pd Nanosheet Aerogel Catalyst with Hybrid Strain Induced by Laser Irradiation and P Doping Postprocess

Strain engineering of electrocatalysts provides an effective strategy to improve the intrinsic catalytic activity. Here, the defect-rich crystalline/amorphous Pd nanosheet aerogel with hybrid microstrain and lattice strain is synthesized by combining laser irradiation and phosphorus doping methods. The surface strain exhibited by the microstrain and lattice strain shifts the d-band center of the electrocatalyst, enhancing the adsorption of intermediates in the ethanol oxidation reaction and thus improving the catalytic performances. The measured mass activity, specific activity and C1-path selectivity of the Pd nanosheet aerogel are 4.48, 3.06, and 5.06 times higher than those of commercial Pd/C, respectively. These findings afford a new strategy for the preparation of highl activity and C1 pathway selective catalysts and provide insight into the catalytic mechanism of strain-rich heterojunction materials based on tunable surface strain values.

Predictive Theoretical Model for the Selective Electroreduction of Nitrate to Ammonia

The electrochemical reduction of nitrate (eNO₃RR) emerges as a promising route for decentralized ammonia synthesis. However, the competitive production of nitrite at low overpotentials is a challenging issue. Herein, using the combination of density functional theory and microkinetic modeling, we show that the selectivity for NH₃ surpasses that of NO₂^- at -0.66 V_RHE, which nicely reproduced the experimental value on titania. NH₂OH* → NH₂* is the kinetically controlling step at a low overpotential for NH₃ generation, while NO₂* → HNO₂ has the highest barrier to producing nitrite. Based on these mechanistic insights, we suggest that ΔG₁ (NH₂OH* → NH₂*) - ΔG₂ (NO₂* → HNO₂) can serve as a descriptor to predict the S(NO₂^-)/S(NH₃) crossover potential. Such a model is verified by the experimental results on Ag, Cu, TiO_2-x, Fe₃O₄, and Fe-MoS₂ and can be extended to the Au catalyst. Thus, this work sheds light on the rational design of catalysts that are simultaneously energy-efficient and selective to NH₃.

Dynamic Electrochemical Interfaces for Energy Conversion and Storage

Electrochemical energy conversion and storage are central to developing future renewable energy systems. For efficient energy utilization, both the performance and stability of electrochemical systems should be optimized in terms of the electrochemical interface. To achieve this goal, it is imperative to understand how a tailored electrode structure and electrolyte speciation can modify the electrochemical interface structure to improve its properties. However, most approaches describe the electrochemical interface in a static or frozen state. Although a simple static model has long been adopted to describe the electrochemical interface, atomic and molecular level pictures of the interface structure should be represented more dynamically to understand the key interactions. From this perspective, we highlight the importance of understanding the dynamics within an electrochemical interface in the process of designing highly functional and robust energy conversion and storage systems. For this purpose, we explore three unique classes of dynamic electrochemical interfaces: self-healing, active-site-hosted, and redox-mediated interfaces. These three cases of dynamic electrochemical interfaces focusing on active site regeneration collectively suggest that our understanding of electrochemical systems should not be limited to static models but instead expanded toward dynamic ones with close interactions between the electrode surface, dissolved active sites, soluble species, and reactants in the electrolyte. Only when we begin to comprehend the fundamentals of these dynamics through operando analyses can electrochemical conversion and storage systems be advanced to their full potential.

Preparation of 2D Molybdenum Phosphide via Surface-Confined Atomic Substitution

The emerging nonlayered 2D materials (NL2DMs) are sparking immense interest due to their fascinating physicochemical properties and enhanced performance in many applications. NL2DMs are particularly favored in catalytic applications owing to the extremely large surface area and low-coordinated surface atoms. However, the synthesis of NL2DMs is complex because their crystals are held together by strong isotropic covalent bonds. Here, nonlayered molybdenum phosphide (MoP) with well-defined 2D morphology is synthesized from layered molybdenum dichalcogenides via surface-confined atomic substitution. During the synthesis, the molybdenum dichalcogenide nanosheet functions as the host matrix where each layer of Mo maintains their hexagonal arrangement and forms isotropic covalent bonds with P that substitutes S, resulting in the conversion from layered van der Waals material to a covalently bonded NL2DM. The MoP nanosheets converted from few-layer MoS₂ are single crystalline, while those converted from monolayers are amorphous. The converted MoP demonstrates metallic charge transport and desirable performance in the electrocatalytic hydrogen evolution reaction (HER). More importantly, in contrast to MoS₂ , which shows edge-dominated HER performance, the edge and basal plane of MoP deliver similar HER performance, which is correlated with theoretical calculations. This work provides a new synthetic strategy for high-quality nonlayered materials with well-defined 2D morphology for future exploration.

How the Bioinspired Fe2Mo6S8 Chevrel Breaks Electrocatalytic Nitrogen Reduction Scaling Relations

The nitrogen reduction reaction (NRR) is a renewable alternative to the energy- and CO₂-intensive Haber-Bosch NH₃ synthesis process but is severely limited by the low activity and selectivity of studied electrocatalysts. The Chevrel phase Fe₂Mo₆S₈ has a surface Fe-S-Mo coordination environment that mimics the nitrogenase FeMo-cofactor and was recently shown to provide state-of-the-art activity and selectivity for NRR. Here, we elucidate the previously unknown NRR mechanism on Fe₂Mo₆S₈ via grand-canonical density functional theory (GC-DFT) that realistically models solvated and biased surfaces. Fe sites of Fe₂Mo₆S₈ selectively stabilize the key *NNH intermediate via a narrow band of free-atom-like surface d-states that selectively hybridize with p-states of *NNH, which results in Fe sites breaking NRR scaling relationships. These sharp d-states arise from an Fe-S bond dissociation during N₂ adsorption that mimics the mechanism of the nitrogenase FeMo-cofactor. Furthermore, we developed a new GC-DFT-based approach for calculating transition states as a function of bias (GC-NEB) and applied it to produce a microkinetic model for NRR at Fe₂Mo₆S₈ that predicts high activity and selectivity, in close agreement with experiments. Our results suggest new design principles that may identify effective NRR electrocatalysts that minimize the barriers for *N₂ protonation and *NH₃ desorption and that may be broadly applied to the rational discovery of stable, multinary electrocatalysts for other reactions where narrow bands of surface d-states can be tuned to selectively stabilize key reaction intermediates and guide selectivity toward a target product. Furthermore, our results highlight the importance of using GC-DFT and GC-NEB to accurately model electrocatalytic reactions.