Global Optimization of Adsorbate Covered Supported Cluster Catalysts: The Case of Pt 7 H 10 CH 3 on α‐Al 2 O 3

Pt12H24 -: A Cuboctahedral Platinum Hydride Cluster Cage

The platinum hydride cluster Pt12H24 - is studied in gas phase by a combination of trapped ion electron diffraction and density functional theory computations. We find a cuboctahedral platinum cage with bridge bound hydrogen atoms. This unusual structure is stabilized by Pt-H-Pt multicenter bonds and shows characteristics of spherical aromaticity.

A configuration sampling study of reaction intermediates constituting catalytic cycles for CO oxidation with Pt1/TiO2

We combine ab initio molecular dynamics (AIMD) simulations with an unsupervised machine learning approach to automate the search for possible configurations of CO oxidation reaction intermediates catalyzed by the atomically dispersed Pt1/TiO2 catalyst. Following the example of Roncoroni and co-workers [Phys. Chem. Chem. Phys. 25, 13741 (2023)], we employ t-distributed stochastic neighbor embedding and hierarchical density-based spatial clustering of applications with noise to reduce the dimensionality and cluster AIMD snapshots based on the local coordination environment of Pt. We identify new local minima, particularly in cases where CO2 is bound to the active site, because it can coordinate in various ways with both the metal and support. The new minima constitute additional elementary steps in some proposed pathways for CO oxidation, resulting in turnover frequencies that differ from prior estimates by several orders of magnitude. This work, therefore, demonstrates that configuration sampling is a necessary component of computational studies of catalytic cycles for atomically dispersed catalysts.

Recalibrating the Experimentally Derived Structure of the Metastable Surface Oxide on Copper via Machine Learning-Accelerated In Silico Global Optimization

The oxidation of copper and its surface oxides are gaining increasing attention due to the enhanced CO2 reduction reaction (CO2RR) activity exhibited by partially oxidized copper among the copper-based catalysts. The "8" surface oxide on Cu(111) is seen as a promising structure for further study due to its resemblance to the highly active Cu2O(110) surface in the C-C coupling of the CO2RR, setting it apart from other O/Cu(111) surface oxides resembling Cu2O(111). However, recent X-ray photoelectron spectroscopy analysis challenges the currently accepted atomic structure of the "8" surface oxide, prompting a need for reevaluation. This study highlights the limitations of conventional methods when addressing such challenges, leading us to adopt global optimization search techniques. After a rigorous process to ensure robustness, the unbiased global minimum of the "8" surface oxide is identified. Interestingly, this configuration differs significantly from other surface oxides and also from previous "8" models while retaining similarities to the Cu2O(110) surface.

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.