Proton-Transfer Mechanisms at the Water–ZnO Interface: The Role of Presolvation

Abstract

The dissociation of water is an important step in many chemical processes at solid surfaces. In particular, water often spontaneously dissociates near metal oxide surfaces, resulting in a mixture of HO, H, and OH at the interface. Ubiquitous proton-transfer (PT) reactions cause these species to dynamically interconvert, but the underlying mechanisms are poorly understood. Here, we develop and use a reactive high-dimensional neural-network potential based on density functional theory data to elucidate the structural and dynamical properties of the interfacial species at the liquid-water-metal-oxide interface, using the nonpolar ZnO(101̅0) surface as a prototypical case. Molecular dynamics simulations reveal that water dissociation and recombination proceed via two types of PT reactions: (i) to and from surface oxide and hydroxide anions ("surface-PT") and (ii) to and from neighboring adsorbed hydroxide ions and water molecules ("adlayer-PT"). We find that the adlayer-PT rate is significantly higher than the surface-PT rate. Water dissociation is, for both types of PT, governed by a predominant presolvation mechanism, i.e., thermal fluctuations that cause the adsorbed water molecules to occasionally accept a hydrogen bond, resulting in a decreased PT barrier and an increased dissociation rate as compared to when no hydrogen bond is present. Consequently, we are able to show that hydrogen bond fluctuations govern PT events at the water-metal-oxide interface in a way similar to that in acidic and basic aqueous bulk solutions.

First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems

“…[28] Other methods are close to reaching this level of applicability.T hese MLPs close ag ap in the toolbox of theoretical chemistry and materials science,i nt hat they can provide very accurate potentials for reactive systems of complex materials.T hey are complementary to established methods in other fields,such as QM/MM methods,which are the method of choice for local chemical reactions.A particular strength of ML methods is the ability to describe all parts of the systems in the same way so that no previous knowledge on the spatial location of the reaction is needed, which is impossible for many systems.T his is particularly useful for simulations in materials science,f or processes at interfaces,a nd for reactions in solutions,i np articular if proton transfer plays ar ole. [101,102] Al ist of HDNNPs which have become dominant in the field of NNPs is shown in Table 1. All these examples show that HDNNPs are suitable for systems as diverse as small molecules, [53] molecular clusters, [103] metal clusters, [104,105] bulk materials, [58] surfaces, [56] water, [55,106] aqueous electrolyte solutions, [107] and solid-liquid interfaces, [108] and they have contributed to new physical insights.…”

From DFT to machine learning: recent approaches to materials science–a review

“…Gaussian approximation potentials (GAPs) have been extensively used to study different systems, such as elemental boron [422], amorphous carbon [423,424], silicon [425], thermal properties of amorphous GeTe and carbon [426], thermomechanics and defects of iron [427], prediction structures of inorganic crystals by combing ML with random search [428], λ-SOAP method for tensorial properties of atomistic systems [247], and a unified framework to predict the properties of materials and molecules such as silicon, organic molecules and proteins ligands [429]. A recent review of applications of high-dimensional neural neural network potentials [430] summarized the notable number of molecular and materials systems studied, which ranges from simple semiconductors such as silicon [233,431,432] and ZnO [433], to more complex systems such as water and metallic clusters [434], molecules [435][436][437], surfaces [438,439], and liquid/solid interfaces [414,440]. Force fields for nanoclusters have been developed with 2-, 3-, and many-body descriptors [441], and the hydrogen adsorption on nanoclusters was described with structural descriptors such as SOAP [442].…”

Machine learning for the modeling of interfaces in energy storage and conversion materials

“…The authors evaluated the free energy barriers for Cu adatom and vacancy diffusion and quantified the structural response of the water near the defects. Quaranta, Hellström, and Behler employed MD simulations to understand the mechanism of proton transfer at the water-ZnO (1010) interface, which led to the identification of a presolvation mechanism previously observed in highly basic solutions [97]. The same authors discovered that the proton-transfer mechanism over the ZnO (1120) surface (Reproduced with permission from [92].…”