Dynamics of Subnanometer Pt Clusters Can Break the Scaling Relationships in Catalysis

Zandkarimi, Borna; Alexandrova, Anastassia N.

doi:10.1021/acs.jpclett.8b03680

Cited by 98 publications

(117 citation statements)

References 61 publications

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“…Cu−O clusters are significantly more flexible than Pd−O, and this allows Cu−O clusters binding the reagents with the affinity similar to that of Pd−O. The stabilization through strong catalyst rearrangement is a special feature of very small clusters (or otherwise highly‐dynamic interfaces), not accessible to more rigid extended surfaces. One of the consequences of this tendency to strongly rearrange is a routine break down of scaling relations in catalysis .…”

Section: Computational Sectionmentioning

confidence: 99%

Oxidative Dehydrogenation of Cyclohexane by Cu vs Pd Clusters: Selectivity Control by Specific Cluster Dynamics

Halder

Zhai

et al. 2020

ChemCatChem

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Supported subnanometer clusters can exhibit catalytic properties not observed in their bulk analogues. Partially‐oxidized Pd and Cu clusters are reported to catalyze the oxidative dehydrogenation of cyclohexane with high activity, and with distinctly different selectivity, producing primarily benzene or cyclohexene, respectively. Under the appliedreaction conditions, the structure and oxidation state of the two catalysts evolve differently, which leads to either the desorption of the cyclohexene intermediate or to its deeper dehydrogenation. Under the applied reaction conditions, the initially oxidized Pd and Cu clusters undergo partial reduction, which we show to be required for the selectivity to emerge. Both systems also have thermal access to multiple distinct structural forms yielding statistical ensembles. The structures within these ensembles evolve with the changing nature of the bound reaction intermediates differently for the two metals; the evolution is found pronounced in the Cu clusters, but only modest in Pd. Ultimately, the different selectivity observed experimentally for the Cu versus Pd clusters is controlled by differences in the collective structural and redox dynamics of their ensembles.

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Section: Computational Sectionmentioning

confidence: 99%

Oxidative Dehydrogenation of Cyclohexane by Cu vs Pd Clusters: Selectivity Control by Specific Cluster Dynamics

Halder

Zhai

et al. 2020

ChemCatChem

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“…Scaling relationships impose fundamental limitations on the catalyst maximal performance, and so there is a continuous hunt for ways of circumventing them. In our recent study, we showed that small Pt clusters do not necessarily follow a highly correlated linear relation and can break the scaling relations, opening opportunities for outstanding catalytic performance. Across small Pt cluster sizes, in the gas phase and when supported on graphene, cluster structure changes dramatically and adsorbate binding site (atop or bridge) also changes, when binding O, OH, and OOH (intermediates in ORR), and upon varying coverage.…”

Section: Implications Of the Ensemble Nature And Dynamic Fluxionalitymentioning

confidence: 99%

Surface‐supported cluster catalysis: Ensembles of metastable states run the show

Zandkarimi

Alexandrova

2019

WIREs Comput Mol Sci

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It has recently been shown that the dynamic behavior of surface‐supported nanocluster catalysts in realistic reaction conditions defies conventional models used in catalysis. This opens new doors in catalysis by giving more leverage in catalyst design, but also requires a major revision of the understanding of how dynamic heterogeneous catalytic interfaces operate, as well as of the computational approaches of catalyst modeling, and experimental methods of catalyst characterization. Major aspects of the new paradigm include the collective action of many catalyst states that form a statistical ensemble in reaction conditions, the catalytic activity and selectivity being driven by rare and metastable catalyst states, reaction thermodynamics and kinetics being controlled by different states of the catalyst, broken scaling relationships, non‐Arrhenius behaviors, and catalyst dynamic restructuring being an essential part of the reaction mechanism. For computation, this complexity means the departure from the standard density functional theory calculations of reaction mechanisms on a single catalyst structure. For experiment, it calls for the development of operando characterization tools with the per‐site resolution and the ability to find the minority sites that govern the catalytic activity. For catalyst design, the goal becomes the creation of the catalyst state (geometric and electronic) that might not be present in the as‐prepared catalyst, but would develop in the reaction conditions and would have the desired activity then. While cluster catalysts are the most dramatic in their dynamic fluxionality, other amorphous interfaces also exhibit some of it, and thus are also subject to similar paradigm revision. This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis

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“…It is in this area that nonlinear ML models may have the greatest promise to accelerate high-throughput screening due to the weak predictive capability of linear scaling relations, which are frequently distorted or broken in isolated, undercoordinated metal sites. 67,105,106 In this work, we train ML models to predict spin-statedependent metal−oxo formation energies in octahedral model catalysts. Using these models, we reveal unexpected structure− property trends in spin-state-dependent reactivity and the limits of the relationships between metal−oxo formation and a conventionally used QM descriptor (i.e., a frontier orbital energy).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation

et al. 2019

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Metal−oxo moieties are important catalytic intermediates in the selective partial oxidation of hydrocarbons and in water splitting. Stable metal−oxo species have reactive properties that vary depending on the spin state of the metal, complicating the development of structure−property relationships. To overcome these challenges, we train machine-learning (ML) models capable of predicting metal−oxo formation energies across a range of first-row metals, oxidation states, and spin states. Using connectivity-only features tailored for inorganic chemistry as inputs to kernel ridge regression or artificial neural network (ANN) ML models, we achieve good mean absolute errors (4−5 kcal/mol) on set-aside test data across a range of ligand orientations. Analysis of feature importance for oxo formation energy prediction reveals the dominance of nonlocal, electronic ligand properties in contrast to other transition metal complex properties (e.g., spin-state or ionization potential). We enumerate the theoretical catalyst space with an ANN, revealing expected trends in oxo formation energetics, such as destabilization of the metal−oxo species with increasing d-filling, as well as exceptions, such as weak correlations with indicators of oxidative stability of the metal in the resting state or unexpected spin-state dependence in reactivity. We carry out uncertainty-aware evolutionary optimization using the ANN to explore a >37 000 candidate catalyst space. New metal and oxidation state combinations are uncovered and validated with density functional theory (DFT), including counterintuitive oxo formation energies for oxidatively stable complexes. This approach doubles the density of confirmed DFT leads in originally sparsely populated regions of property space, highlighting the potential of ML-model-driven discovery to uncover catalyst design rules and exceptions.

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Dynamics of Subnanometer Pt Clusters Can Break the Scaling Relationships in Catalysis

Cited by 98 publications

References 61 publications

Oxidative Dehydrogenation of Cyclohexane by Cu vs Pd Clusters: Selectivity Control by Specific Cluster Dynamics

Oxidative Dehydrogenation of Cyclohexane by Cu vs Pd Clusters: Selectivity Control by Specific Cluster Dynamics

Surface‐supported cluster catalysis: Ensembles of metastable states run the show

Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation

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