Achievements and Expectations in the Field of Computational Heterogeneous Catalysis in an Innovation Context

A possibility to infer frustrations of supported catalytic clusters from macro-scale observations

Recent experimental and theoretical studies suggest that dynamic active centres of supported heterogeneous catalysts may, under certain conditions, be frustrated. Such out-of-equilibrium materials are expected to possess unique catalytic properties and also higher level of functionality. The latter is associated with the navigation through the free energy landscapes with energetically close local minima. The lack of common approaches to the study of out-of-equilibrium materials motivates the search for specific ones. This paper suggests a way to infer some valuable information from the interplay between the intensity of reagent supply and regularities of product formation.

Reference-Quality Free Energy Barriers in Catalysis from Machine Learning Thermodynamic Perturbation Theory

For the first time, we report calculations of the free energies of activation of cracking and isomerization reactions of alkenes that combine several different electronic structure methods with molecular dynamics simulations. We demonstrate that the use of a high level of theory (here Random Phase Approximation-RPA) is necessary to bridge the gap between experimental and computed values. These transformations, catalyzed by zeolites and proceeding via cationic intermediates and transition states, are building blocks of many chemical transformations for valorization of long chain paraffins originating, e.g., from plastic waste, vegetable oils, Fischer-Tropsch waxes or crude oils. Compared with the free energy barriers computed at the PBE+D2 production level of theory via constrained ab initio molecular dynamics, the barriers computed at the RPA level by the application of Machine Learning thermodynamic Perturbation Theory (MLPT) show a significant decrease for isomerization reaction and an increase of a similar magnitude for cracking, yielding an unprecedented agreement with the results obtained by experiments and kinetic modeling.

Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations

Within this Perspective, we critically reflect on the role of first-principles molecular dynamics (MD) simulations in unraveling the catalytic function within zeolites under operating conditions. First-principles MD simulations refer to methods where the dynamics of the nuclei is followed in time by integrating the Newtonian equations of motion on a potential energy surface that is determined by solving the quantum-mechanical many-body problem for the electrons. Catalytic solids used in industrial applications show an intriguing high degree of complexity, with phenomena taking place at a broad range of length and time scales. Additionally, the state and function of a catalyst critically depend on the operating conditions, such as temperature, moisture, presence of water, etc. Herein we show by means of a series of exemplary cases how first-principles MD simulations are instrumental to unravel the catalyst complexity at the molecular scale. Examples show how the nature of reactive species at higher catalytic temperatures may drastically change compared to species at lower temperatures and how the nature of active sites may dynamically change upon exposure to water. To simulate rare events, first-principles MD simulations need to be used in combination with enhanced sampling techniques to efficiently sample low-probability regions of phase space. Using these techniques, it is shown how competitive pathways at operating conditions can be discovered and how broad transition state regions can be explored. Interestingly, such simulations can also be used to study hindered diffusion under operating conditions. The cases shown clearly illustrate how first-principles MD simulations reveal insights into the catalytic function at operating conditions, which could not be discovered using static or local approaches where only a few points are considered on the potential energy surface (PES). Despite these advantages, some major hurdles still exist to fully integrate first-principles MD methods in a standard computational catalytic workflow or to use the output of MD simulations as input for multiple length/time scale methods that aim to bridge to the reactor scale. First of all, methods are needed that allow us to evaluate the interatomic forces with quantum-mechanical accuracy, albeit at a much lower computational cost compared to currently used density functional theory (DFT) methods. The use of DFT limits the currently attainable length/time scales to hundreds of picoseconds and a few nanometers, which are much smaller than realistic catalyst particle dimensions and time scales encountered in the catalysis process. One solution could be to construct machine learning potentials (MLPs), where a numerical potential is derived from underlying quantum-mechanical data, which could be used in subsequent MD simulations. As such, much longer length and time scales could be reached; however, quite some research is still necessary to construct MLPs for the complex systems encountered in industrially used catalysts. Second, most currently used enhanced sampling techniques in catalysis make use of collective variables (CVs), which are mostly determined based on chemical intuition. To explore complex reactive networks with MD simulations, methods are needed that allow the automatic discovery of CVs or methods that do not rely on a priori definition of CVs. Recently, various data-driven methods have been proposed, which could be explored for complex catalytic systems. Lastly, first-principles MD methods are currently mostly used to investigate local reactive events. We hope that with the rise of data-driven methods and more efficient methods to describe the PES, first-principles MD methods will in the future also be able to describe longer length/time scale processes in catalysis. This might lead to a consistent dynamic description of all steps-diffusion, adsorption, and reaction-as they take place at the catalyst particle level.

Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling

Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are rungs on a multiscale workflow. Establishing connections across these rungs and integration with experiments have been challenging. Here, operando catalyst structure prediction techniques using density functional theory simulations and ab initio thermodynamics calculations, molecular dynamics, and machine learning techniques are presented. Surface structure characterization by computational spectroscopic and machine learning techniques is then discussed. Hierarchical approaches in kinetic parameter estimation involving semi-empirical, data-driven, and first-principles calculations and detailed kinetic modeling via mean-field microkinetic modeling and kinetic Monte Carlo simulations are discussed along with methods and the need for uncertainty quantification. With these as the background, this article proposes a bottom-up hierarchical and closed loop modeling framework incorporating consistency checks and iterative refinements at each level and across levels.

Mechanisms for Catalytic CO Oxidation on SiAun (n = 1-5) Cluster

Significant progress has been made in understanding the reactivity and catalytic activity of gas-phase and loaded gold clusters for CO oxidation. However, little research has focused on mixed silicon/gold clusters (SiAu_n) for CO oxidation. In the present work, we performed density function theory (DFT) calculations for a SiAu_n (n = 1-5) cluster at the CAM-B3LYP/aug-cc-pVDZ-PP level and investigated the effects on the reactivity and catalytic activity of the SiAu_n cluster for CO oxidation. The calculated results show that the effect is very low for the activation barriers for the formation of OOCO intermediates on SiAu clusters, SiAu₃ clusters, and SiAu₅ clusters in the catalytic oxidation of CO and the activation energy barriers for the formation of OCO intermediates on OSiAu₃, OSiAu₄, and OSiAu₅. Our calculations show that, compared with the conventional small Au cluster, the incorporation of Si enhances the catalytic performance towards CO oxidation.

Zn Modification of Pd/TiO2/Ti Catalyst for CO Oxidation

The main goal of this study was to modify the activity of Pd/TiO₂/Ti catalyst in the reaction of CO oxidation by the addition of Zn. Plasma electrolytic oxidation (PEO) of Ti wire was conducted to produce a uniform porous layer of TiO₂. A mixture of Pd and Zn was then introduced by means of adsorption. After reduction treatment, the activity of the samples was examined by oxidation of 5% CO in a temperature range from 80-350 °C. Model catalysts with sufficient amounts of the metals for physico-chemical investigation were prepared to further investigate the reaction between Pd and Zn during CO oxidation. The structures and compositions of the samples were investigated using scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS), time-of-flight secondary ion mass spectrometry (TOF-SIMS), inductively coupled plasma mass spectrometry (ICP-MS), Fourier transform infrared (FTIR), and X-ray diffraction (XRD). Modification of Pd/TiO₂/Ti catalyst by Zn with a Pd:Zn atomic ratio of 2:1 decreased the temperature of complete CO oxidation from 220 °C for Pd/TiO₂/Ti to 180 °C for Pd-Zn/TiO₂/Ti. The temperature of 50% CO conversion on Pd-Zn(2:1)/TiO₂/Ti was around 55 °C lower than in the reaction on monometallic Pd catalyst. The addition of Zn to the Pd catalyst lowered the binding energy of CO on the surface and improved the dissociative adsorption of oxygen, facilitating the oxidation of CO. FTIR showed that the bridging form of adsorbed CO is preferred on bimetallic systems. Analysis of the surface compositions of the samples (SEM-EDS, TOF-SIMS) showed higher amounts of oxygen on the bimetallic systems.

Gas Adsorption in Zeolite and Thin Zeolite Layers: Molecular Simulation, Experiment, and Adsorption Potential Theory

Molecular simulations and experiments are used to investigate methane adsorption in bulk and thin layers of MFI zeolite (silicalite-1). After comparing the theoretical adsorption data obtained using Grand Canonical Monte Carlo simulations for bulk MFI at various temperatures against experiments, zeolite layers with different crystalline orientations and levels of surface flexibility are considered. The data obtained for such prototypical systems allow us to rationalize both the qualitative and quantitative impact of external surface in nanoporous solids. In particular, due to strong confinement in zeolite pores, methane is found to adsorb at low pressures in the core of the zeolite while external surface adsorption occurs at pressures where the internal porosity of zeolite is saturated. Using Polanyi's adsorption potential theory, which is derived here from Hill's general scheme for adsorption, we provide a simple thermodynamic formalism to predict consistently adsorption both in the internal porosity and at the external surface of nanoporous solids. While this seminal theory has been already applied for gases in nanoporous solids, its extension to describe both surface and volume adsorption is important to provide a general rational framework for fluid adsorption in finely divided materials. We also discuss the applicability of this formalism for gas adsorption data under supercritical conditions.

Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status, and challenges

Kinetic Monte Carlo (KMC) simulations in combination with first-principles (1p)-based calculations are rapidly becoming the gold-standard computational framework for bridging the gap between the wide range of length scales and time scales over which heterogeneous catalysis unfolds. 1p-KMC simulations provide accurate insights into reactions over surfaces, a vital step toward the rational design of novel catalysts. In this Perspective, we briefly outline basic principles, computational challenges, successful applications, as well as future directions and opportunities of this promising and ever more popular kinetic modeling approach.

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

Autonomous catalysis research requires elaborate integration of operando experiments into automated workflows. Suitable experimental data for analysis by artificial intelligence can be measured more readily according to standard operating procedures.