Recent Progress toward Ab Initio Modeling of Electrocatalysis

Constant Potential Thermodynamic Integration for Obtaining the Free Energy Profile of Electrochemical Reaction

In this work, we advanced an efficient free energy sampling method based on constrained ab initio molecular dynamics (cAIMD) with a fully explicit solvent layer to depict the electrochemical reaction process at constant surface charge density, named the "Constant-Potential Thermodynamic Integration (CPTI)" method. For automatically adjusting surface charge density at different states, we built an "on-the-fly" procedure which is capable of managing all the necessary steps during cAIMD simulations, including the system pre-equilibrium, surface charge density updating, and force sampling. We applied it to predict the potential-dependent free energy profiles of CO2 adsorption on a single-atom catalyst. The results show that our method can not only account for changes in electrostatic potential energy associated with potential but also consider the potential-induced solvation effects. Our approach enables the accurate simulation of electrochemical environment by presenting the complete solid-liquid interface and efficient computation of electrocatalytic reaction energetics based on a robust potential descriptor.

Crystal-Phase Engineering in Heterogeneous Catalysis

The performance of a chemical reaction is critically dependent on the electronic and/or geometric structures of a material in heterogeneous catalysis. Over the past century, the Sabatier principle has already provided a conceptual framework for optimal catalyst design by adjusting the electronic structure of the catalytic material via a change in composition. Beyond composition, it is essential to recognize that the geometric atomic structures of a catalyst, encompassing terraces, edges, steps, kinks, and corners, have a substantial impact on the activity and selectivity of a chemical reaction. Crystal-phase engineering has the capacity to bring about substantial alterations in the electronic and geometric configurations of a catalyst, enabling control over coordination numbers, morphological features, and the arrangement of surface atoms. Modulating the crystallographic phase is therefore an important strategy for improving the stability, activity, and selectivity of catalytic materials. Nonetheless, a complete understanding of how the performance depends on the crystal phase of a catalyst remains elusive, primarily due to the absence of a molecular-level view of active sites across various crystal phases. In this review, we primarily focus on assessing the dependence of catalytic performance on crystal phases to elucidate the challenges and complexities inherent in heterogeneous catalysis, ultimately aiming for improved catalyst design.

Realistic Modeling of the Electrocatalytic Process at Complex Solid-Liquid Interface

The rational design of electrocatalysis has emerged as one of the most thriving means for mitigating energy and environmental crises. The key to this effort is the understanding of the complex electrochemical interface, wherein the electrode potential as well as various internal factors such as H-bond network, adsorbate coverage, and dynamic behavior of the interface collectively contribute to the electrocatalytic activity and selectivity. In this context, the authors have reviewed recent theoretical advances, and especially, the contributions to modeling the realistic electrocatalytic processes at complex electrochemical interfaces,  and illustrated the challenges and fundamental problems in this field. Specifically, the significance of the inclusion of explicit solvation and electrode potential as well as the strategies toward the design of highly efficient electrocatalysts are discussed. The structure-activity relationships and their dynamic responses to the environment and catalytic functionality under working conditions are illustrated to be crucial factors for understanding the complexed interface and the electrocatalytic activities. It is hoped that this review can help spark new research passion and ultimately bring a step closer to a realistic and systematic modeling method for electrocatalysis.

Electric Double Layer Effects in Electrocatalysis: Insights from Ab Initio Simulation and Hierarchical Continuum Modeling

Structures of the electric double layer (EDL) at electrocatalytic interfaces, which are modulated by the material properties, the electrolyte characteristics (e.g., the pH, the types and concentrations of ions), and the electrode potential, play crucial roles in the reaction kinetics. Understanding the EDL effects in electrocatalysis has attracted substantial research interest in recent years. However, the intrinsic relationships between the specific EDL structures and electrocatalytic kinetics remain poorly understood, especially on the atomic scale. In this Perspective, we briefly review the recent advances in deciphering the EDL effects mainly in hydrogen and oxygen electrocatalysis through a multiscale approach, spanning from the atomistic scale simulated by ab initio methods to the macroscale by a hierarchical approach. We highlight the importance of resolving the local reaction environment, especially the local hydrogen bond network, in understanding EDL effects. Finally, some of the remaining challenges are outlined, and an outlook for future developments in these exciting frontiers is provided.

Operando studies of Mn oxide based electrocatalysts for the oxygen evolution reaction

Inspired by photosystem II (PS II), Mn oxide based electrocatalysts have been repeatedly investigated as catalysts for the electrochemical oxygen evolution reaction (OER), the anodic reaction in water electrolysis. However, a comparison of the conditions in biological OER catalysed by the water splitting complex CaMn4Ox with the requirements for an electrocatalyst for industrially relevant applications reveals fundamental differences. Thus, a systematic development of artificial Mn-based OER catalysts requires both a fundamental understanding of the catalytic mechanisms as well as an evaluation of the practicality of the system for industrial scale applications. Experimentally, both aspects can be approached using in situ and operando methods including spectroscopy. This paper highlights some of the major challenges common to different operando investigation methods and recent insights gained with them. To this end, vibrational spectroscopy, especially Raman spectroscopy, absorption techniques in the bandgap region and operando X-ray spectroelectrochemistry (SEC), both in the hard and soft X-ray regime are particularly focused on here. Technical challenges specific to each method are discussed first, followed by challenges that are specific to Mn oxide based systems. Finally, recent in situ and operando studies are reviewed. This analysis shows that despite the technical and Mn specific challenges, three specific key features are common to most of the studied systems with significant OER activity: structural disorder, Mn oxidation states between III and IV, and the appearance of layered birnessite phases in the active regime.

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.

Evaluating Adsorbate-Solvent Interactions: Are Dispersion Corrections Necessary?

Incorporating solvent-adsorbate interactions is paramount in models of aqueous (electro)catalytic reactions. Although a number of techniques exist, they are either highly demanding in computational terms or inaccurate. Microsolvation offers a trade-off between accuracy and computational expenses. Here, we dissect a method to swiftly outline the first solvation shell of species adsorbed on transition-metal surfaces and assess their corresponding solvation energy. Interestingly, dispersion corrections are generally not needed in the model, but caution is to be exercised when water-water and water-adsorbate interactions are of similar magnitude.

Machine Learning Assisted Simulations of Electrochemical Interfaces: Recent Progress and Challenges

The electrochemical interface, where the adsorption of reactants and electrocatalytic reactions take place, has long been a focus of attention. Some of the important processes on it tend to possess relatively slow kinetic characteristics, which are usually beyond the scope of ab initio molecular dynamics. The newly emerging technique, machine learning methods, provides an alternative approach to achieve thousands of atoms and nanosecond time scale while ensuring precision and efficiency. In this Perspective, we summarize in detail the recent progress and achievements made by the introduction of machine learning to simulate electrochemical interfaces, and focus on the limitations of current machine learning models, such as accurate descriptions of long-range electrostatic interactions and the kinetics of the electrochemical reactions occurring at the interface. Finally, we further point out the future directions for machine learning to expand in the field of electrochemical interfaces.

Insight into the Interaction of Furfural with Metallic Surfaces in the Electrochemical Hydrogenation Process

Electrocatalytic hydrogenation of furfural on metal surfaces has become an important research subject due to the potential of the reaction product 2-methylfuran as a renewable energy resource. Identifying effective determinants in this reaction process requires a thorough investigation of the complex electrode-electrolyte interactions, which considers a variety of the influential components. In this work, in operando electrochemical Raman Spectroscopy and Molecular Dynamics simulations were utilized to investigate different characteristics of the interface layer in the electrocatalytic hydrogenation of furfural. Hereby, the influence of applied potentials, electrode material, and electrolyte composition were investigated in detail. The studied parameters give an insight into furfural's binding situation, molecular orientation, and reaction mechanism.

Materials Screening by the Descriptor G max(η): The Free-Energy Span Model in Electrocatalysis

To move from fossil-based energy resources to a society based on renewables, electrode materials free of precious noble metals are required to efficiently catalyze electrochemical processes in fuel cells, batteries, or electrolyzers. Materials screening operating at minimal computational cost is a powerful method to assess the performance of potential electrode compositions based on heuristic concepts. While the thermodynamic overpotential in combination with the volcano concept refers to the most popular descriptor-based analysis in the literature, this notion cannot reproduce experimental trends reasonably well. About two years ago, the concept of G _max(η), based on the idea of the free-energy span model, has been proposed as a universal approach for the screening of electrocatalysts. In contrast to other available descriptor-based methods, G _max(η) factors overpotential and kinetic effects by a dedicated evacuation scheme of adsorption free energies into an analysis of trends. In the present perspective, we discuss the application of G _max(η) to different electrocatalytic processes, including the oxygen evolution and reduction reactions, the nitrogen reduction reaction, and the selectivity problem of the competing oxygen evolution and peroxide formation reactions, and we outline the advantages of this screening approach over previous investigations.

Machine Learning: A New Paradigm in Computational Electrocatalysis

Designing and screening novel electrocatalysts, understanding electrocatalytic mechanisms at an atomic level, and uncovering scientific insights lie at the center of the development of electrocatalysis. Despite certain success in experiments and computations, it is still difficult to achieve the above objectives due to the complexity of electrocatalytic systems and the vastness of the chemical space for candidate electrocatalysts. With the advantage of machine learning (ML) and increasing interest in electrocatalysis for energy conversion and storage, data-driven scientific research motivated by artificial intelligence (AI) has provided new opportunities to discover promising electrocatalysts, investigate dynamic reaction processes, and extract knowledge from huge data. In this Perspective, we summarize the recent applications of ML in electrocatalysis, including the screening of electrocatalysts and simulation of electrocatalytic processes. Furthermore, interpretable machine learning methods for electrocatalysis are discussed to accelerate knowledge generation. Finally, the blueprint of machine learning is envisaged for future development of electrocatalysis.

Realistic Modelling of Dynamics at Nanostructured Interfaces Relevant to Heterogeneous Catalysis

The focus of this short review is directed towards investigations of the dynamics of nanostructured metallic heterogeneous catalysts and the evolution of interfaces during reaction—namely, the metal–gas, metal–liquid, and metal–support interfaces. Indeed, it is of considerable interest to know how a metal catalyst surface responds to gas or liquid adsorption under reaction conditions, and how its structure and catalytic properties evolve as a function of its interaction with the support. This short review aims to offer the reader a birds-eye view of state-of-the-art methods that enable more realistic simulation of dynamical phenomena at nanostructured interfaces by exploiting resource-efficient methods and/or the development of computational hardware and software.