Exploring Pretreatment-Morphology Relationships: Ab Initio Wulff Construction for RuO2Nanoparticles under Oxidising Conditions

Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials

The surface properties of solid-state materials often dictate their functionality, especially for applications where nanoscale effects become important. The relevant surface(s) and their properties are determined, in large part, by the material's synthesis or operating conditions. These conditions dictate thermodynamic driving forces and kinetic rates responsible for yielding the observed surface structure and morphology. Computational surface science methods have long been applied to connect thermochemical conditions to surface phase stability, particularly in the heterogeneous catalysis and thin film growth communities. This review provides a brief introduction to first-principles approaches to compute surface phase diagrams before introducing emerging data-driven approaches. The remainder of the review focuses on the application of machine learning, predominantly in the form of learned interatomic potentials, to study complex surfaces. As machine learning algorithms and large datasets on which to train them become more commonplace in materials science, computational methods are poised to become even more predictive and powerful for modeling the complexities of inorganic surfaces at the nanoscale.

Epitaxial Core-Shell Oxide Nanoparticles: First-Principles Evidence for Increased Activity and Stability of Rutile Catalysts for Acidic Oxygen Evolution

Due to their high activity and favorable stability in acidic electrolytes, Ir and Ru oxides are primary catalysts for the oxygen evolution reaction (OER) in proton-exchange membrane (PEM) electrolyzers. For a future large-scale application, core-shell nanoparticles are an appealing route to minimize the demand for these precious oxides. Here, we employ first-principles density-functional theory (DFT) and ab initio thermodynamics to assess the feasibility of encapsulating a cheap rutile-structured TiO₂ core with coherent, monolayer-thin IrO₂ or RuO₂ films. Resulting from a strong directional dependence of adhesion and strain, a wetting tendency is only obtained for some low-index facets under typical gas-phase synthesis conditions. Thermodynamic stability in particular of lattice-matched RuO₂ films is instead indicated for more oxidizing conditions. Intriguingly, the calculations also predict an enhanced activity and stability of such epitaxial RuO₂ /TiO₂ core-shell particles under OER operation.

Data-efficient iterative training of Gaussian approximation potentials: Application to surface structure determination of rutile IrO2 and RuO2

Machine-learning interatomic potentials, such as Gaussian Approximation Potentials (GAPs), constitute a powerful class of surrogate models to computationally involved first-principles calculations. At a similar predictive quality but significantly reduced cost, they could leverage otherwise barely tractable extensive sampling as in global surface structure determination (SSD). This efficiency is jeopardized though, if an a priori unknown structural and chemical search space as in SSD requires an excessive number of first-principles data for the GAP training. To this end, we present a general and data-efficient iterative training protocol that blends the creation of new training data with the actual surface exploration process. Demonstrating this protocol with the SSD of low-index facets of rutile IrO₂ and RuO₂, the involved simulated annealing on the basis of the refining GAP identifies a number of unknown terminations even in the restricted sub-space of (1 × 1) surface unit cells. Particularly in an O-poor environment, some of these, then metal-rich terminations, are thermodynamically most stable and are reminiscent of complexions as discussed for complex ceramic materials.

Density functional theory studies of transition metal carbides and nitrides as electrocatalysts

Transition metal carbides and nitrides are interesting non-precious materials that have been shown to replace or reduce the loading of precious metals for catalyzing several important electrochemical reactions. The purpose of this review is to summarize density functional theory (DFT) studies, describe reaction pathways, identify activity and selectivity descriptors, and present a future outlook in designing carbide and nitride catalysts for the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), nitrogen reduction reaction (N₂RR), CO₂ reduction reaction (CO₂RR) and alcohol oxidation reactions. This topic is of high interest to scientific communities working in the field of electrocatalysis and this review should provide theoretical guidance for the rational design of improved carbide and nitride electrocatalysts.

Single-atom catalyst for high-performance methanol oxidation

Single-atom catalysts have been widely investigated for several electrocatalytic reactions except electrochemical alcohol oxidation. Herein, we synthesize atomically dispersed platinum on ruthenium oxide (Pt₁/RuO₂) using a simple impregnation-adsorption method. We find that Pt₁/RuO₂ has good electrocatalytic activity towards methanol oxidation in an alkaline media with a mass activity that is 15.3-times higher than that of commercial Pt/C (6766 vs. 441 mA mg^‒1_Pt). In contrast, single atom Pt on carbon black is inert. Further, the mass activity of Pt₁/RuO₂ is superior to that of most Pt-based catalysts previously developed. Moreover, Pt₁/RuO₂ has a high tolerance towards CO poisoning, resulting in excellent catalytic stability. Ab initio simulations and experiments reveal that the presence of Pt‒O_3f (3-fold coordinatively bonded O)‒Ru_cus (coordinatively unsaturated Ru) bonds with the undercoordinated bridging O in Pt₁/RuO₂ favors the electrochemical dehydrogenation of methanol with lower energy barriers and onset potential than those encountered for Pt‒C and Pt‒Ru.

It is still challenging to engineer single-atom catalysts for electrocatalytic methanol oxidation. Here, the authors design Pt single atom supported on RuO2 for highly active methanol oxidation in contrast to the inert Pt single atom supported on carbon.

IrO_{2} Surface Complexions Identified through Machine Learning and Surface Investigations

A Gaussian approximation potential was trained using density-functional theory data to enable a global geometry optimization of low-index rutile IrO_{2} facets through simulated annealing. Ab initio thermodynamics identifies (101) and (111) (1×1) terminations competitive with (110) in reducing environments. Experiments on single crystals find that (101) facets dominate and exhibit the theoretically predicted (1×1) periodicity and x-ray photoelectron spectroscopy core-level shifts. The obtained structures are analogous to the complexions discussed in the context of ceramic battery materials.

SQERT-T: alleviating kinetic Monte Carlo (KMC)-stiffness in transient KMC simulations

Lattice based kinetic Monte Carlo (KMC) is often used for simulating the dynamics of systems at a supramolecular scale, based on molecular scale transitions. A common challenge in KMC simulations is rapid 'back-and-forth' reactions, which dominate the events executed during simulations and inhibit the ability for simulations to reach longer time scales. Such processes are fast frivolous processes (FFPs) and are one manifestation of a phenomenon referred to as KMC-stiffness. Here, an algorithm for staggered quasi-equilibrium rank-based throttling geared towards transient kinetics (SQERT-T) is presented. Within the SQERT-T methodology, a pace-restrictor reaction and an FFP floor are utilized along with throttling of the process transition rate constants to accelerate the KMC simulations while still retaining sufficient time resolution for sampling of the data. KMC simulations were performed for CO oxidation over RuO₂(1 1 0) and over RuO₂(1 1 1), and the results were compared to experimental data obtained using RuO₂ powders. The experiments and simulations were for transient conditions: the system was subjected to a temperature program which included temperatures in the range of 363 to 453 K. The timescales that were achieved during the KMC simulations in this study would not have been accessible without KMC acceleration, and were enabled by the use of SQERT-T.

Temperature-induced transformation of electrochemically formed hydrous RuO2 layers over Ru(0001) model electrodes

Hydrous RuO2 reveals excellent performance both as a supercapacitor and as a heterogeneous oxidation catalyst. Molecular understanding of these processes needs, however, a model system with preferably low structural and morphological complexity. This goal is partly accomplished here by using single crystalline Ru(0001) as a template on which hydrous RuO2 is electrochemically formed. The hydrous RuO2 layers on Ru(0001) and their temperature induced transformation under ultra high vacuum (UHV) conditions are comprehensively characterized by scanning electron microscopy and X-ray photoemission spectroscopy. The hydrous RuO2 layer grows with an intricate morphology governed by the presence of step bunching regions of the Ru(0001) surface. Upon annealing to 200 °C in UHV the hydrous RuO2 layer transforms mostly into flat metallic Ru islands and occasionally into (100) and (111) oriented RuO2 particles aligned along the high symmetry direction of Ru(0001).

Theoretical Heterogeneous Catalysis: Scaling Relationships and Computational Catalyst Design

Scaling relationships are theoretical constructs that relate the binding energies of a wide variety of catalytic intermediates across a range of catalyst surfaces. Such relationships are ultimately derived from bond order conservation principles that were first introduced several decades ago. Through the growing power of computational surface science and catalysis, these concepts and their applications have recently begun to have a major impact in studies of catalytic reactivity and heterogeneous catalyst design. In this review, the detailed theory behind scaling relationships is discussed, and the existence of these relationships for catalytic materials ranging from pure metal to oxide surfaces, for numerous classes of molecules, and for a variety of catalytic surface structures is described. The use of the relationships to understand and elucidate reactivity trends across wide classes of catalytic surfaces and, in some cases, to predict optimal catalysts for certain chemical reactions, is explored. Finally, the observation that, in spite of the tremendous power of scaling relationships, their very existence places limits on the maximum rates that may be obtained for the catalyst classes in question is discussed, and promising strategies are explored to overcome these limitations to usher in a new era of theory-driven catalyst design.

Nanoscale Origin of Mesoscale Roughening: Real-Time Tracking and Identification of Three Distinct Ruthenium Oxide Phases in Ruthenium Oxidation

The structural modification of the Ru(0001) surface is followed in real-time using low-energy electron microscopy at elevated temperatures during exposure to molecular oxygen. We observe the nucleation and growth of three different RuO2 facets, which are unambiguously identified by single-domain microspot low-energy electron diffraction (μLEED) analysis from regions of 250 nm in diameter. Structural identification is then pushed to the true nanoscale by employing very-low-energy electron reflectivity spectra R(E) from regions down to 10 nm for structural fingerprinting of complex reactions such as the oxidation of metal surfaces. Calculations of R(E) with an ab initio scattering theory confirm the growth of (110), (100), and (101) orientations of RuO2 and explain the shape of the R(E) spectra in terms of the conducting band structure. This methodology is ideally suited to identify the structure of supported ultrathin films and dynamic transformations at multicomponent interfaces down to few nanometer lateral resolution at elevated temperature and in reactive environments.