Molecular O2 Dimers and Lattice Instability in a Perovskite Electrocatalyst.

Structural degradation of oxide electrodes during the electrocatalytic oxygen evolution reaction (OER) is a major challenge in water electrolysis. Although the OER is known to induce changes in the surface layer, little is known about its effect on the bulk of the electrocatalyst and its overall phase stability. Here, we show that under OER conditions, a highly active SrCoO3-x electrocatalyst develops bulk lattice instability, which results in the formation of molecular O2 dimers inside the bulk and nanoscale amorphization induced via chemo-mechanical coupling. Using high-resolution resonant inelastic X-ray scattering and first-principles calculations, we unveil the potential-dependent evolution of lattice oxygen inside the perovskite and demonstrate that O2 dimers are stable in a densely packed crystal lattice, thus challenging the assumption that O2 dimers require sufficient interatomic spacing. We also show that the energy cost of local atomic rearrangements in SrCoO3-x becomes very low under the OER conditions, leading to an unusual amorphization under intercalation-induced stress. As a result, we propose that the amorphization energy can be calculated from the first principles and can be used to assess the stability of electrocatalysts. Our study demonstrates that extreme oxidation of electrocatalysts under OER can intrinsically destabilize the lattice and result in bulk anion redox and disorder, suggesting why some oxide materials are unstable and develop a thick amorphous layer under water electrolysis conditions.

Step-induced double-row pattern of interfacial water on rutile TiO2(110) under electrochemical conditions.

Metal oxides are promising (photo)electrocatalysts for sustainable energy technologies due to their good activity and abundant resources. Their applications such as photocatalytic water splitting predominantly involve aqueous interfaces under electrochemical conditions, but in situ probing oxide-water interfaces is proven to be extremely challenging. Here, we present an electrochemical scanning tunneling microscopy (EC-STM) study on the rutile TiO2(110)-water interface, and by tuning surface redox chemistry with careful potential control we are able to obtain high quality images of interfacial structures with atomic details. It is interesting to find that the interfacial water exhibits an unexpected double-row pattern that has never been observed. This finding is confirmed by performing a large scale simulation of a stepped interface model enabled by machine learning accelerated molecular dynamics (MLMD) with ab initio accuracy. Furthermore, we show that this pattern is induced by the steps present on the surface, which can propagate across the terraces through interfacial hydrogen bonds. Our work demonstrates that by combining EC-STM and MLMD we can obtain new atomic details of interfacial structures that are valuable to understand the activity of oxides under realistic conditions.

Physical adsorption of OH- causes anomalous charging at oxide-water interfaces.

This study reveals a charging mechanism at oxide-water interfaces, solving the puzzle that challenges the traditional electrical double layer (EDL) model. We found that the experimentally measured zeta potential is caused by physically adsorbed OH-, instead of acidic dissociation of surface OHs and the first-layer water. This mechanism should apply for a wide range of material interfaces and could find applications in future.

ChecMatE: A workflow package to automatically generate machine learning potentials and phase diagrams for semiconductor alloys.

Semiconductor alloy materials are highly versatile due to their adjustable properties; however, exploring their structural space is a challenging task that affects the control of their properties. Traditional methods rely on ad hoc design based on the understanding of known chemistry and crystallography, which have limitations in computational efficiency and search space. In this work, we present ChecMatE (Chemical Material Explorer), a software package that automatically generates machine learning potentials (MLPs) and uses global search algorithms to screen semiconductor alloy materials. Taking advantage of MLPs, ChecMatE enables a more efficient and cost-effective exploration of the structural space of materials and predicts their energy and relative stability with ab initio accuracy. We demonstrate the efficacy of ChecMatE through a case study of the InxGa1-xN system, where it accelerates structural exploration at reduced costs. Our automatic framework offers a promising solution to the challenging task of exploring the structural space of semiconductor alloy materials.

Resolving the odd-even oscillation of water dissociation at rutile TiO2(110)-water interface by machine learning accelerated molecular dynamics.

Aqueous rutile TiO2(110) is the most widely studied water-oxide interface, and yet questions about water dissociation are still controversial. Theoretical studies have systematically investigated the influence of the slab thickness on water dissociation energy (Ediss) at 1 monolayer coverage using static density functional theory calculation and found that Ediss exhibits odd-even oscillation with respect to the TiO2 slab thickness. However, less studies have accounted for the full solvation of an aqueous phase using ab initio molecular dynamics due to high computational costs in which only three, four, and five trilayer models of rutile(110)-water interfaces have been simulated. Here, we report Machine Learning accelerated Molecular Dynamics (MLMD) simulations of defect-free rutile(110)-water interfaces, which allows for a systematic study of the slab thickness ranging from 3 to 17 trilayers with much lower costs while keeping ab initio accuracy. Our MLMD simulations show that the dissociation degree of surface water (α) oscillates with the slab thickness and converges to ∼2% as the TiO2 slab becomes thicker. Converting α into dissociation free energy (ΔAdiss) and comparing with dissociation total energy Ediss calculated with a single monolayer of water, we find that the full solvation of the interfaces suppresses surface water from dissociating. It is interesting to note that the machine learning potential trained from the dataset containing exclusively the five trilayer TiO2 model exhibits excellent transferability to other slab thicknesses and further captures the oscillating behavior of surface water dissociation. Detailed analyses indicate that the central plane in odd trilayer slabs modulates the interaction between double trilayers and, thus, the bonding strength between terminal Ti and water, which affects pKa of surface water and water dissociation degree.

Recent Progress toward Ab Initio Modeling of Electrocatalysis.

Electrode potential is the key factor for controlling electrocatalytic reactions at electrochemical interfaces, and moreover, it is also known that the pH and solutes (e.g., cations) of the solution have prominent effects on electrocatalysis. Understanding these effects requires microscopic information on the electrochemical interfaces, in which theoretical simulations can play an important role. This Perspective summarizes the recent progress in method development for modeling electrochemical interfaces, including different methods for describing the electrolytes at the interfaces and different schemes for charging up the electrode surfaces. In the final section, we provide an outlook for future development in modeling methods and their applications to electrocatalysis.