CO organization at ambient pressure on stepped Pt surfaces: first principles modeling accelerated by neural networks

Effect of Dynamic and Preferential Decoration of Pt Catalyst Surfaces by WOx on Hydrodeoxygenation Reactions

Catalysts containing Pt nanoparticles and reducible transition-metal oxides (WOx, NbOx, TiOx) exhibit remarkable selectivity to aromatic products in hydrodeoxygenation (HDO) reactions for biomass valorization, contrasting the undesired aromatic hydrogenation typically observed for metal catalysts. However, the active site(s) responsible for the high selectivity remains elusive. Here, theoretical and experimental analyses are combined to explain the observed HDO reactivity by interrogating the organization of reduced WOx domains on Pt surfaces at sub-monolayer coverage. The SurfGraph algorithm is used to develop model structures that capture the configurational space (∼1000 configurations) for density functional theory (DFT) calculations of a W3O7 trimer on stepped Pt surfaces. Machine-learning models trained on the DFT calculations identify the preferential occupation of well-coordinated Pt sites (≥8 Pt coordination number) by WOx and structural features governing WOx-Pt stability. WOx/Pt/SiO2 catalysts are synthesized with varying W loadings to test the theoretical predictions and relate them to HDO reactivity. Spectroscopy- and microscopy-based catalyst characterizations identify the dynamic and preferential decoration of well-coordinated sites on Pt nanoparticles by reduced WOx species, consistent with theoretical predictions. The catalytic consequences of this preferential decoration on the HDO of a lignin model compound, dihydroeugenol, are clarified. The effect of WOx decoration on Pt nanoparticles for HDO involves WOx inhibition of aromatic ring hydrogenation by preferentially blocking well-coordinated Pt sites. The identification of preferential decoration on specific sites of late-transition-metal surfaces by reducible metal oxides provides a new perspective for understanding and controlling metal-support interactions in heterogeneous catalysis.

Modeling Interfacial Dynamics on Single Atom Electrocatalysts: Explicit Solvation and Potential Dependence

ConspectusSingle atom electrocatalysts, with noble metal-free composition, maximal atom efficiency, and exceptional reactivity toward various energy and environmental applications, have become a research hot spot in the recent decade. Their simplicity and the isolated nature of the atomic structure of their active site have also made them an ideal model catalyst system for studying reaction mechanisms and activity trends. However, the state of the single atom active sites during electrochemical reactions may not be as simple as is usually assumed. To the contrary, the single atom electrocatalysts have been reported to be under greater influence from interfacial dynamics, with solvent and electrolyte ions perpetually interacting with the electrified active center under an applied electrode potential. These complexities render the activity trends and reaction mechanisms derived from simplistic models dubious.In this Account, with a few popular single atom electrocatalysis systems, we show how the change in electrochemical potential induces nontrivial variation in the free energy profile of elemental electrochemical reaction steps, demonstrate how the active centers with different electronic structure features can induce different solvation structures at the interface even for the same reaction intermediate of the simplest electrochemical reaction, and discuss the implication of the complexities on the kinetics and thermodynamics of the reaction system to better address the activity and selectivity trends. We also venture into more intriguing interfacial phenomena, such as alternative reaction pathways and intermediates that are favored and stabilized by solvation and polarization effects, long-range interfacial dynamics across the region far beyond the contact layer, and the dynamic activation or deactivation of single atom sites under operation conditions. We show the necessity of including realistic aspects (explicit solvent, electrolyte, and electrode potential) into the model to correctly capture the physics and chemistry at the electrochemical interface and to understand the reaction mechanisms and reactivity trends. We also demonstrate how the popular simplistic design principles fail and how they can be revised by including the kinetics and interfacial factors in the model. All of these rich dynamics and chemistry would remain hidden or overlooked otherwise. We believe that the complexity at an electrochemical interface is not a curse but a blessing in that it enables deeper understanding and finer control of the potential-dependent free energy landscape of electrochemical reactions, which opens up new dimensions for further design and optimization of single atom electrocatalysts and beyond. Limitations of current methods and challenges faced by the theoretical and experimental communities are discussed, along with the possible solutions awaiting development in the future.

Atomic-Scale Mechanism of Platinum Catalyst Restructuring under a Pressure of Reactant Gas

Heterogeneous catalysis is key for chemical transformations. Understanding how catalysts' active sites dynamically evolve at the atomic scale under reaction conditions is a prerequisite for accurately determining catalytic mechanisms and predictably developing catalysts. We combine in situ time-dependent scanning tunneling microscopy observations and machine-learning-accelerated first-principles atomistic simulations to uncover the mechanism of restructuring of Pt catalysts under a pressure of carbon monoxide (CO). We show that a high CO coverage at a Pt step edge triggers the formation of atomic protrusions of low-coordination Pt atoms, which then detach from the step edge to create sub-nano-islands on the terraces, where under-coordinated sites are stabilized by the CO adsorbates. The fast and accurate machine-learning potential is key to enabling the exploration of tens of thousands of configurations for the CO-covered restructuring catalyst. These studies open an avenue to achieve an atomic-scale understanding of the structural dynamics of more complex metal nanoparticle catalysts under reaction conditions.

Ensemble representation of catalytic interfaces: soloists, orchestras, and everything in-between

Catalytic systems are complex and dynamic, exploring vast chemical spaces on multiple timescales. In this perspective, we discuss the dynamic behavior of fluxional, heterogeneous thermal and electrocatalysts and the ensembles of many isomers which govern their behavior. We develop a new paradigm in catalysis theory in which highly fluxional systems, namely sub-nano clusters, isomerize on a much shorter timescale than that of the catalyzed reaction, so macroscopic properties arise from the thermal ensemble of isomers, not just the ground state. Accurate chemical predictions can only be reached through a many-structure picture of the catalyst, and we explain the breakdown of conventional methods such as linear scaling relations and size-selected prevention of sintering. We capitalize on the forward-looking discussion of the means of controlling the size of these dynamic ensembles. This control, such that the most effective or selective isomers can dominate the system, is essential for the fluxional catalyst to be practicable, and their targeted synthesis to be possible. It will also provide a fundamental lever of catalyst design. Finally, we discuss computational tools and experimental methods for probing ensembles and the role of specific isomers. We hope that catalyst optimization using chemically informed descriptors of ensemble nature and size will become a new norm in the field of catalysis and have broad impacts in sustainable energy, efficient chemical production, and more.

Atomistic Global Optimization X: A Python package for optimization of atomistic structures

Modelling and understanding properties of materials from first principles require knowledge of the underlyingatomistic structure. This entails knowing the individual chemical identity and position of all atoms involved.Obtaining such information for macro-molecules, nano-particles, clusters, and for the surface, interface, andbulk phases of amorphous and solid materials represents a difficult high-dimensional global optimizationproblem. The rise of machine learning techniques in materials science has, however, led to many compellingdevelopments that may speed up structure searches. The complexity of such new methods has prompted aneed for an efficient way of assembling them into global optimization algorithms that can be experimentedwith. In this paper, we introduce the Atomistic Global Optimization X (AGOX) framework and code, asa customizable approach that enables efficient building and testing of global optimization algorithms. Amodular way of expressing global optimization algorithms is described and modern programming practicesare used to enable that modularity in the freely available AGOX python package. A number of examplesof global optimization approaches are implemented and analyzed. This ranges from random search andbasin-hopping to machine learning aided approaches with on-the-fly learnt surrogate energy landscapes. Themethods are show-cased on problems ranging from supported clusters over surface reconstructions to largecarbon clusters and metal-nitride clusters incorporated into graphene sheets.