What Changes on the Inverse Catalyst? Insights from CO Oxidation on Au-Supported Ceria Nanoparticles Using Ab Initio Molecular Dynamics

Bird's-Eye View of the Activity Distribution on a Catalyst Surface via a Machine Learning-Driven Adequate Sampling Algorithm

Rational design of catalysts relies on a deep understanding of the active centers. The structure and activity distribution of active centers on a surface are two of the central issues in catalysis and important targets of theoretical and experimental investigations. Herein, we report a machine learning-driven adequate sampling (MLAS) framework for obtaining a statistical understanding of the chemical environment near catalyst sites. Combined strategies were implemented to achieve highly efficient sampling, including the decomposition of degrees of freedom, stratified sampling, Gaussian process regression, and well-designed constraint optimization. The MLAS framework was applied to the rate-determining step in NH3 synthesis, namely the N2 activation process. We calculated the produced population function, PA, which provides a comprehensive and intuitive understanding of active centers. The MLAS framework can be broadly applied to other more complicated catalyst materials and reaction networks.

Toward Electrocatalytic Methanol Oxidation Reaction: Longstanding Debates and Emerging Catalysts

The study of direct methanol fuel cells (DMFCs) has lasted around 70 years, since the first investigation in the early 1950s. Though enormous effort has been devoted in this field, it is still far from commercialization. The methanol oxidation reaction (MOR), as a semi-reaction of DMFCs, is the bottleneck reaction that restricts the overall performance of DMFCs. To date, there has been intense debate on the complex six-electron reaction, but barely any reviews have systematically discussed this topic. To this end, the controversies and progress regarding the electrocatalytic mechanisms, performance evaluations as well as the design science toward MOR electrocatalysts are summarized. This review also provides a comprehensive introduction on the recent development of emerging MOR electrocatalysts with a focus on the innovation of the alloy, core-shell structure, heterostructure, and single-atom catalysts. Finally, perspectives on the future outlook toward study of the mechanisms and design of electrocatalysts are provided.

Measuring and directing charge transfer in heterogenous catalysts

Precise control of charge transfer between catalyst nanoparticles and supports presents a unique opportunity to enhance the stability, activity, and selectivity of heterogeneous catalysts. While charge transfer is tunable using the atomic structure and chemistry of the catalyst-support interface, direct experimental evidence is missing for three-dimensional catalyst nanoparticles, primarily due to the lack of a high-resolution method that can probe and correlate both the charge distribution and atomic structure of catalyst/support interfaces in these structures. We demonstrate a robust scanning transmission electron microscopy (STEM) method that simultaneously visualizes the atomic-scale structure and sub-nanometer-scale charge distribution in heterogeneous catalysts using a model Au-catalyst/SrTiO₃-support system. Using this method, we further reveal the atomic-scale mechanisms responsible for the highly active perimeter sites and demonstrate that the charge transfer behavior can be readily controlled using post-synthesis treatments. This methodology provides a blueprint for better understanding the role of charge transfer in catalyst stability and performance and facilitates the future development of highly active advanced catalysts.

Addressing Dynamics at Catalytic Heterogeneous Interfaces with DFT-MD: Anomalous Temperature Distributions from Commonly Used Thermostats

Density functional theory-based molecular dynamics (DFT-MD) has been widely used for studying the chemistry of heterogeneous interfacial systems under operational conditions. We report frequently overlooked errors in thermostated or constant-temperature DFT-MD simulations applied to study (electro)catalytic chemistry. Our results demonstrate that commonly used thermostats such as Nosé-Hoover, Berendsen, and simple velocity-rescaling methods fail to provide a reliable temperature description for systems considered. Instead, nonconstant temperatures and large temperature gradients within the different parts of the system are observed. The errors are not a "feature" of any particular code but are present in several ab initio molecular dynamics implementations. This uneven temperature distribution, due to inadequate thermostatting, is well-known in the classical MD community, where it is ascribed to the failure in kinetic energy equipartition among different degrees of freedom in heterogeneous systems (Harvey et al. J. Comput. Chem. 1998, 726-740) and termed the flying ice cube effect. We provide tantamount evidence that interfacial systems are susceptible to substantial flying ice cube effects and demonstrate that the traditional Nosé-Hoover and Berendsen thermostats should be applied with care when simulating, for example, catalytic properties or structures of solvated interfaces and supported clusters. We conclude that the flying ice cube effect in these systems can be conveniently avoided using Langevin dynamics.

Selective olefin production on silica based iron catalysts in Fischer–Tropsch synthesis

Mixed phases of Fe3O4 and Fe5C2, interacting properly with SiO2, produce highly selective olefins from syngas.