Computing Surface Acidity Constants of Proton Hopping Groups from Density Functional Theory-Based Molecular Dynamics: Application to the SnO2(110)/H2O Interface

Data-Efficient Active Learning for Thermodynamic Integration: Acidity Constants of BiVO4 in Water

The protonation state of molecules and surfaces is pivotal in various disciplines, including (electro-)catalysis, geochemistry, biochemistry, and pharmaceutics. Accurately and efficiently determining acidity constants is critical yet challenging, particularly when explicitly considering the electronic structure, thermal fluctuations, anharmonic vibrations, and solvation effects. In this research, we employ thermodynamic integration accelerated by committee Neural Network potentials, training a single machine learning model that accurately describes the relevant protonated, deprotonated, and intermediate states. We investigate two deprotonation reactions at the BiVO4 (010)-water interface, a promising candidate for efficient photocatalytic water splitting. Our results illustrate the convergence of the required ensemble averages over simulation time and of the final acidity constant as a function of the Kirkwood coupling parameter. We demonstrate that simulation times on the order of nanoseconds are required for statistical convergence. This time scale is currently unachievable with explicit ab-initio molecular dynamics simulations at the hybrid DFT level of theory. In contrast, our machine learning workflow only requires a few hundred DFT single point calculations for training and testing. Exploiting the extended time scales accessible, we furthermore asses the effect of commonly applied bias potentials. Thus, our study significantly advances calculating free energy differences with ab-initio accuracy.

Water-Mediated Proton Hopping Mechanisms at the SnO2(110)/H2O Interface from Ab Initio Deep Potential Molecular Dynamics

The interfacial proton transfer (PT) reaction on the metal oxide surface is an important step in many chemical processes including photoelectrocatalytic water splitting, dehydrogenation, and hydrogen storage. The investigation of the PT process, in terms of thermodynamics and kinetics, has received considerable attention, but the individual free energy barriers and solvent effects for different PT pathways on rutile oxide are still lacking. Here, by applying a combination of ab initio and deep potential molecular dynamics methods, we have studied interfacial PT mechanisms by selecting the rutile SnO2(110)/H2O interface as an example of an oxide with the characteristic of frequently interfacial PT processes. Three types of PT pathways among the interfacial groups are found, i.e., proton transfer from terminal adsorbed water to bridge oxygen directly (surface-PT) or via a solvent water (mediated-PT), and proton hopping between two terminal groups (adlayer PT). Our simulations reveal that the terminal water in mediated-PT prefers to point toward the solution and forms a shorter H-bond with the assisted solvent water, leading to the lowest energy barrier and the fastest relative PT rate. In particular, it is found that the full solvation environment plays a crucial role in water-mediated proton conduction, while having little effect on direct PT reactions. The PT mechanisms on aqueous rutile oxide interfaces are also discussed by comparing an oxide series composed of SnO2, TiO2, and IrO2. Consequently, this work provides valuable insights into the ability of a deep neural network to reproduce the ab initio potential energy surface, as well as the PT mechanisms at such oxide/liquid interfaces, which can help understand the important chemical processes in electrochemistry, photoelectrocatalysis, colloid science, and geochemistry.

Band alignment of CoO(100)-water and CoO(111)-water interfaces accelerated by machine learning potentials

Cobalt monoxide (CoO) nanomaterials have drawn attention for their remarkable photocatalytic water splitting without an externally applied potential or co-catalyst. The success of overall water splitting is due to the appropriate band edge positions of the catalyst, which span the redox potentials of water splitting. Typically, CoO nanomaterials possess complex morphologies, which consist of multiple active surfaces. As a result, the precise roles of the surfaces in the overall water-splitting process remain to be elucidated. In this work, we have undertaken a thorough investigation into the band alignments at the CoO(100)-water and CoO(111)-water interfaces using ab initio molecular dynamics and machine learning accelerated molecular dynamics simulations. The results of band alignment reveal that CoO(100) supports both the Hydrogen Evolution Reaction (HER) and the oxygen evolution reaction, whereas CoO(111) only facilitates the HER. Moreover, the variance in band positions between CoO(100) and CoO(111) results in an intrinsic potential difference, facilitating the migration of electrons toward CoO(100), while holes accumulate on CoO(111). The separation of photoexcited carriers effectively promotes water splitting in CoO.

Molecular picture of electric double layers with weakly adsorbed water

Water adsorption energy, Eads, is a key physical quantity in sustainable chemical technologies such as (photo)electrocatalytic water splitting, water desalination, and water harvesting. In many of these applications, the electrode surface is operated outside the point (potential) of zero charge, which attracts counter-ions to form the electric double layer and controls the surface properties. Here, by applying density functional theory-based finite-field molecular dynamics simulations, we have studied the effect of water adsorption energy Eads on surface acidity and the Helmholtz capacitance of BiVO4 as an example of metal oxide electrodes with weakly chemisorbed water. This allows us to establish the effect of Eads on the coordination number, the H-bond network, and the orientation of chemisorbed water by comparing an oxide series composed of BiVO4, TiO2, and SnO2. In particular, it is found that a positive correlation exists between the degree of asymmetry ΔCH in the Helmholtz capacitance and the strength of Eads. This correlation is verified and extended further to graphene-like systems with physisorbed water, where the electric double layers (EDLs) are controlled by electronic charge rather than proton charge as in the oxide series. Therefore, this work reveals a general relationship between water adsorption energy Eads and EDLs, which is relevant to both electrochemical reactivity and the electrowetting of aqueous interfaces.

Molecular insights into the water dissociation and proton dynamics at the β-TaON (100)/water interface

Understanding the dynamic nature of the semiconductor-water interface is crucial for developing efficient photoelectrochemical water splitting catalysts, as it governs reactivity through charge and mass transport. In this study, we employ ab initio molecular dynamics simulations to investigate the structural and dynamical properties of water at the β-TaON (100) surface. We observed that a well-defined interface is established through the spontaneous dissociation of water and the reorganization of surface chemical bonds. This leads to the formation of a partially hydroxylated surface, accompanied by a strong network of hydrogen bonds at the TaON-water interface. Consequently, various proton transport routes, including the proton transfer through "low-barrier hydrogen bond" path, become active across the interface, dramatically increasing the overall rate of the proton hopping at the interface. Based on our findings, we propose that the observed high photocatalytic activity of TaON-based semiconductors could be attributed to the spontaneous water dissociation and the resulting high proton transfer rate at the interface.

Modeling of Nanomaterials for Supercapacitors: Beyond Carbon Electrodes

Capacitive storage devices allow for fast charge and discharge cycles, making them the perfect complements to batteries for high power applications. Many materials display interesting capacitive properties when they are put in contact with ionic solutions despite their very different structures and (surface) reactivity. Among them, nanocarbons are the most important for practical applications, but many nanomaterials have recently emerged, such as conductive metal-organic frameworks, 2D materials, and a wide variety of metal oxides. These heterogeneous and complex electrode materials are difficult to model with conventional approaches. However, the development of computational methods, the incorporation of machine learning techniques, and the increasing power in high performance computing now allow us to tackle these types of systems. In this Review, we summarize the current efforts in this direction. We show that depending on the nature of the materials and of the charging mechanisms, different methods, or combinations of them, can provide desirable atomic-scale insight on the interactions at play. We mainly focus on two important aspects: (i) the study of ion adsorption in complex nanoporous materials, which require the extension of constant potential molecular dynamics to multicomponent systems, and (ii) the characterization of Faradaic processes in pseudocapacitors, that involves the use of electronic structure-based methods. We also discuss how recently developed simulation methods will allow bridges to be made between double-layer capacitors and pseudocapacitors for future high power electricity storage devices.

Accelerating Computation of Acidity Constants and Redox Potentials for Aqueous Organic Redox Flow Batteries by Machine Learning Potential-Based Molecular Dynamics

Due to the increased concern about energy and environmental issues, significant attention has been paid to the development of large-scale energy storage devices to facilitate the utilization of clean energy sources. The redox flow battery (RFB) is one of the most promising systems. Recently, the high cost of transition-metal complex-based RFB has promoted the development of aqueous RFBs with redox-active organic molecules. To expand the working voltage, computational chemistry has been applied to search for organic molecules with lower or higher redox potentials. However, redox potential computation based on implicit solvation models would be challenging due to difficulty in parametrization when considering the complex solvation of supporting electrolytes. Besides, although ab initio molecular dynamics (AIMD) describes the supporting electrolytes with the same level of electronic structure theory as the redox couple, the application is impeded by the high computation costs. Recently, machine learning molecular dynamics (MLMD) has been illustrated to accelerate AIMD by several orders of magnitude without sacrificing the accuracy. It has been established that redox potentials can be computed by MLMD with two separated machine learning potentials (MLPs) for reactant and product states, which is redundant and inefficient. In this work, an automated workflow is developed to construct a universal MLP for both states, which can compute the redox potentials or acidity constants of redox-active organic molecules more efficiently. Furthermore, the predicted redox potentials can be evaluated at the hybrid functional level with much lower costs, which would facilitate the design of aqueous organic RFBs.

Water effect on the band edges of anatase TiO2 surfaces: A theoretical study on charge migration across surface heterojunctions and facet-dependent photoactivity

The charge migration mechanism across the surface heterojunction constructed on an anatase TiO2 nanocrystal is still under debate. To solve this longstanding question, we present a systematic study of the band edges (vs. standard hydrogen electrode, SHE) of aqueous TiO2 interfaces with anatase (101), (100) and (001) surfaces, using a combination of density functional theory-based molecular dynamics (DFTMD) and efficient computational SHE (cSHE) methods. Our calculations show that the conduction band minimum (CBM) of the (101) surface is lower than that of (001) and (100) surfaces, which is thermodynamically favorable for electrons migrating to the (101) surface through the surface heterojunction, while the hole preferentially accumulates on the (100) surface due to its highest valence band minimum (VBM). In addition, we qualitatively explore the facet-dependent photocatalytic activity of anatase TiO2. Due to the possession of both the beneficial atomic structure (with 100% undercoordinated Ti5c atoms at the surface) and electronic structure (more strongly oxidizing holes in the VBM and efficient electron-hole spatial separation separation), the (001) surface exhibits the most efficient photocatalytic performance for water oxidation. Furthermore, it is confirmed that the use of simplified theoretical models neglecting the detailed atomic structures of water at the aqueous interface is inadequate to predict the band alignment of semiconductors relative to water redox potentials, so that it may result in substantial errors in evaluating the photocatalytic performance of materials to be used for water splitting.

Acid-Base Chemistry of a Model IrO2 Catalytic Interface

Iridium oxide (IrO2) is one of the most efficient catalytic materials for the oxygen evolution reaction (OER), yet the atomic scale structure of its aqueous interface is largely unknown. Herein, the hydration structure, proton transfer mechanisms, and acid-base properties of the rutile IrO2(110)-water interface are investigated using ab initio based deep neural-network potentials and enhanced sampling simulations. The proton affinities of the different surface sites are characterized by calculating their acid dissociation constants, which yield a point of zero charge in agreement with experiments. A large fraction (≈80%) of adsorbed water dissociation is observed, together with a short lifetime (≈0.5 ns) of the resulting terminal hydroxy groups, due to rapid proton exchanges between adsorbed H2O and adjacent OH species. This rapid surface proton transfer supports the suggestion that the rate-determining step in the OER may not involve proton transfer across the double layer into solution, as indicated by recent experiments.

Microscopic Simulations of Electrochemical Double-Layer Capacitors

Electrochemical double-layer capacitors (EDLCs) are devices allowing the storage or production of electricity. They function through the adsorption of ions from an electrolyte on high-surface-area electrodes and are characterized by short charging/discharging times and long cycle-life compared to batteries. Microscopic simulations are now widely used to characterize the structural, dynamical, and adsorption properties of these devices, complementing electrochemical experiments and in situ spectroscopic analyses. In this review, we discuss the main families of simulation methods that have been developed and their application to the main family of EDLCs, which include nanoporous carbon electrodes. We focus on the adsorption of organic ions for electricity storage applications as well as aqueous systems in the context of blue energy harvesting and desalination. We finally provide perspectives for further improvement of the predictive power of simulations, in particular for future devices with complex electrode compositions.

Origin of Asymmetric Electric Double Layers at Electrified Oxide/Electrolyte Interfaces

The structure of electric double layers (EDLs) dictates the chemistry and the physics of electrified interfaces, and the differential capacitance is the key property for characterizing EDLs. Here we develop a theoretical model for computing the differential Helmholtz capacitance C_H of oxide-electrolyte interfaces using density functional theory-based finite-field molecular dynamics simulations. It is found that the dipole of interfacial adsorbed groups (i.e., water molecule, hydroxyl ion, and proton) at the electrified SnO₂(110)/NaCl interfaces significantly modulates the double layer potential which leads to the asymmetric distribution of C_H. We also find that the dissociative water adsorption prefers the inner sphere binding of counterions, which in turn leads to a higher Helmholtz capacitance, compared with that of the nondissociative case at the interface. This work provides a molecular interpretation of asymmetric EDLs seen experimentally in a range of metal oxides/hydroxides.