Quantitative Description of Metal Center Organization and Interactions in Single-Atom Catalysts.

Ultra-high-density single-atom catalysts (UHD-SACs) present unique opportunities for harnessing cooperative effects between neighboring metal centers. However, the lack of tools to establish correlations between the density, types, and arrangements of isolated metal atoms and the support surface properties hinders efforts to engineer advanced material architectures. Here, this work precisely describes the metal center organization in various mono- and multimetallic UHD-SACs based on nitrogen-doped carbon (NC) supports by coupling transmission electron microscopy with tailored machine-learning methods (released as a user-friendly web app) and density functional theory simulations. This approach quantifies the non-negligible presence of multimers with increasing atom density, characterizes the size and shape of these low-nuclearity clusters, and identifies surface atom density criteria to ensure isolation. Further, it provides previously inaccessible experimental insights into coordination site arrangements in the NC host, uncovering a repulsive interaction that influences the disordered distribution of metal centers in UHD-SACs. This observation holds in multimetallic systems, where chemically-specific analysis quantifies the degree of intermixing. These fundamental insights into the materials chemistry of single-atom catalysts are crucial for designing catalytic systems with superior reactivity.

Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set.

Machine learning (ML) methods have shown promise for discovering novel catalysts but are often restricted to specific chemical domains. Generalizable ML models require large and diverse training data sets, which exist for heterogeneous catalysis but not for homogeneous catalysis. The tmQM data set, which contains properties of 86,665 transition metal complexes calculated at the TPSSh/def2-SVP level of density functional theory (DFT), provided a promising training data set for homogeneous catalyst systems. However, we find that ML models trained on tmQM consistently underpredict the energies of a chemically distinct subset of the data. To address this, we present the tmQM_wB97MV data set, which filters out several structures in tmQM found to be missing hydrogens and recomputes the energies of all other structures at the ωB97M-V/def2-SVPD level of DFT. ML models trained on tmQM_wB97MV show no pattern of consistently incorrect predictions and much lower errors than those trained on tmQM. The ML models tested on tmQM_wB97MV were, from best to worst, GemNet-T > PaiNN ≈ SpinConv > SchNet. Performance consistently improves when using only neutral structures instead of the entire data set. However, while models saturate with only neutral structures, more data continue to improve the models when including charged species, indicating the importance of accurately capturing a range of oxidation states in future data generation and model development. Furthermore, a fine-tuning approach in which weights were initialized from models trained on OC20 led to drastic improvements in model performance, indicating transferability between ML strategies of heterogeneous and homogeneous systems.

WhereWulff: A Semiautonomous Workflow for Systematic Catalyst Surface Reactivity under Reaction Conditions.

This paper introduces WhereWulff, a semiautonomous workflow for modeling the reactivity of catalyst surfaces. The workflow begins with a bulk optimization task that takes an initial bulk structure and returns the optimized bulk geometry and magnetic state, including stability under reaction conditions. The stable bulk structure is the input to a surface chemistry task that enumerates surfaces up to a user-specified maximum Miller index, computes relaxed surface energies for those surfaces, and then prioritizes those for subsequent adsorption energy calculations based on their contribution to the Wulff construction shape. The workflow handles computational resource constraints such as limited wall-time as well as automated job submission and analysis. We illustrate the workflow for oxygen evolution reaction (OER) intermediates on two double perovskites. WhereWulff nearly halved the number of Density Functional Theory (DFT) calculations from ∼240 to ∼132 by prioritizing terminations, up to a maximum Miller index of 1, based on surface stability. Additionally, it automatically handled the 180 additional resubmission jobs required to successfully converge 120+ atoms systems under a 48-h wall-time cluster constraint. There are four main use cases that we envision for WhereWulff: (1) as a first-principles source of truth to validate and update a closed-loop self-sustaining materials discovery pipeline, (2) as a data generation tool, (3) as an educational tool, allowing users (e.g., experimentalists) unfamiliar with OER modeling to probe materials they might be interested in before doing further in-domain analyses, (4) and finally, as a starting point for users to extend with reactions other than the OER, as part of a collaborative software community.

Atomically Dispersed Iridium on Indium Tin Oxide Efficiently Catalyzes Water Oxidation.

Heterogeneous catalysts in the form of atomically dispersed metals on a support provide the most efficient utilization of the active component, which is especially important for scarce and expensive late transition metals. These catalysts also enable unique opportunities to understand reaction pathways through detailed spectroscopic and computational studies. Here, we demonstrate that atomically dispersed iridium sites on indium tin oxide prepared via surface organometallic chemistry display exemplary catalytic activity in one of the most challenging electrochemical processes, the oxygen evolution reaction (OER). In situ X-ray absorption studies revealed the formation of IrV=O intermediate under OER conditions with an Ir-O distance of 1.83 Å. Modeling of the reaction mechanism indicates that IrV=O is likely a catalyst resting state, which is subsequently oxidized to IrVI enabling fast water nucleophilic attack and oxygen evolution. We anticipate that the applied strategy can be instrumental in preparing and studying a broad range of atomically dispersed transition metal catalysts on conductive oxides for (photo)electrochemical applications.

Water Adsorption on MO2 (M = Ti, Ru, and Ir) Surfaces. Importance of Octahedral Distortion and Cooperative Effects.

Understanding metal oxide MO2 (M = Ti, Ru, and Ir)-water interfaces is essential to assess the catalytic behavior of these materials. The present study analyzes the H2O-MO2 interactions at the most abundant (110) and (011) surfaces, at two different water coverages: isolated water molecules and full monolayer, by means of Perdew-Burke-Ernzerhof-D2 static calculations and ab initio molecular dynamics (AIMD) simulations. Results indicate that adsorption preferably occurs in its molecular form on (110)-TiO2 and in its dissociative form on (110)-RuO2 and (110)-IrO2. The opposite trend is observed at the (011) facet. This different behavior is related to the kind of octahedral distortion observed in the bulk of these materials (tetragonal elongation for TiO2 and tetragonal compression for RuO2 and IrO2) and to the different nature of the vacant sites created, axial on (110) and equatorial on (011). For the monolayer, additional effects such as cooperative H-bond interactions and cooperative adsorption come into play in determining the degree of deprotonation. For TiO2, AIMD indicates that the water monolayer is fully undissociated at both (110) and (011) surfaces, whereas for RuO2, water monolayer exhibits a 50% dissociation, the formation of H3O2 - motifs being essential. Finally, on (110)-IrO2, the main monolayer configuration is the fully dissociated one, whereas on (011)-IrO2, it exhibits a degree of dissociation that ranges between 50 and 75%. Overall, the present study shows that the degree of water dissociation results from a delicate balance between the H2O-MO2 intrinsic interaction and cooperative hydrogen bonding and adsorption effects.