Density Functional Theory Study of the Zeolite-Catalyzed Methylation of Benzene with Methanol

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.

Identifying the key steps determining the selectivity of toluene methylation with methanol over HZSM-5

Methylation of toluene with methanol to produce p-xylene has been investigated for decades, but the origin of selectivity is still under debate. Here we report computational studies based on ab initio molecular dynamics simulations and free energy sampling methods to identify the key steps determining the selectivity. The steps of toluene methylation to protonated-xylene, deprotonation of protonated-xylenes, and diffusion of xylene in HZSM-5 channels are compared. We find the pathways of formation for protonated p-/m-xylenes have similar free energy barriers. Meanwhile, the methylation is found rate-determining, thus the probability to generate p-/m-xylenes at the active site are similar. We then find that the diffusion for m-xylene along the zigzag channel is more difficult than its isomerization to p-xylene, which in turn further promotes the selectivity of p-xylene formation. These insights obtained at the molecular level are crucial for further development of high-performance zeolite catalysts for toluene methylation.

The selectivity of zeolite catalyzed toluene methylation is still under debate. Here the authors report a comprehensive theoretical investigation based on ab-initio molecular dynamics to identify the key-steps of methylation of toluene with methanol over a zeolite to produce p-xylene.

Theoretical investigation of the side-chain mechanism of the MTO process over H-SSZ-13 using DFT and ab initio calculations

The side-chain mechanism of the methanol-to-olefins process over the H-SSZ-13 acidic zeolite was investigated using periodic density functional theory with corrections from highly accurate ab intio calculations on large cluster models.

How Accurately Do Approximate Density Functionals Predict Trends in Acidic Zeolite Catalysis?

Density functional theory (DFT) is increasingly used for computational screening procedures with the aim of finding new catalysts. To achieve this, it is critical that relative differences between materials are predicted with high accuracy. How DFT at the generalized gradient approximation (GGA) level performs in this respect is investigated here for catalytic reactions employing acidic zeotypes using highly accurate DLPNO-CCSD(T) calculations as the reference. This is studied for 65 reaction energies and 130 reaction barriers related to zeolite catalysis. Our results obtained for the PBE-D3 and BEEF-vdW functionals show that while these functionals are prone to large errors, they predict trends occurring from one catalyst to another with an accuracy of about 5 kJ/mol, strongly supporting the widespread use of DFT calculations for the computational screening and design of new catalytic materials.