Machine learning potential era of zeolite simulation

Realizing the Direct Synthesis of High Silica IWS Zeolite with Improved Hydrothermal Stability

Direct synthesis of germanosilicate zeolites with low Ge content and improved hydrothermal stability is a great challenge. Herein, we successfully achieve the direct synthesis of IWS zeolite with a Si/Ge ratio higher than 4 for the first time. High silica IWS zeolites can be prepared in a wide range of Si/Ge ratios (4-16) by utilizing bulky 1,3-bis(1-adamantyl)-imidazolium (BAdaI+) as an efficient organic structure-directing agent from the concentrated synthesis gel under fluoride conditions. It is proven by a series of characterizations that Ge atoms preferentially occupy the double-four-ring (D4R) units. Theoretical calculations reveal the preferential interactions of guest organic structure-directing agents (OSDAs) and host IWS zeolites with different Si/Ge ratios. The introduction of more Ge atoms cannot improve the host-guest interaction when the BAdaI+ molecule is accommodated within the nanopores of IWS zeolite compared to other OSDAs. The obtained IWS zeolite shows an extremely high specific surface area (905 m2/g) and pore volume (1.31 cm3/g). Due to the low Ge content, IWS zeolite exhibits outstanding hydrothermal stability and experiences high temperature steam heating with no loss of crystallinity and only a slight loss of microporosity.

A reactive neural network framework for water-loaded acidic zeolites

Under operating conditions, the dynamics of water and ions confined within protonic aluminosilicate zeolite micropores are responsible for many of their properties, including hydrothermal stability, acidity and catalytic activity. However, due to high computational cost, operando studies of acidic zeolites are currently rare and limited to specific cases and simplified models. In this work, we have developed a reactive neural network potential (NNP) attempting to cover the entire class of acidic zeolites, including the full range of experimentally relevant water concentrations and Si/Al ratios. This NNP has the potential to dramatically improve sampling, retaining the (meta)GGA DFT level accuracy, with the capacity for discovery of new chemistry, such as collective defect formation mechanisms at the zeolite surface. Furthermore, we exemplify how the NNP can be used as a basis for further extensions/improvements which include data-efficient adoption of higher-level (hybrid) references via Δ-learning and the acceleration of rare event sampling via automatic construction of collective variables. These developments represent a significant step towards accurate simulations of realistic catalysts under operando conditions.

Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review

The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.

Stereomutations in silicate oligomerization: the role of steric hindrance and intramolecular hydrogen bonding

Stereomutation has previously been explored in the literature with regard to the different mechanisms and activation energy barriers between different forms. However, the forces which govern stereomutations within the intermediate steps in chemical reactions have not been explored previously, a topic addressed in this article. The process of silicate oligomerization has been chosen in this study and it is demonstrated that steric hindrance of molecules and intramolecular hydrogen bonding govern the stereomutation of intermediate pentacoordinate silicon compounds in the silicate oligomerization process. It could be observed that the combined effect of intramolecular hydrogen bonding and steric hindrance in the silicate oligomerization process facilitates the conventional berry pseudorotation mechanism rather than other pseudorotation mechanisms reported in the literature.

Unveiling the mechanisms of carboxylic acid esterification on acid zeolites for biomass-to-energy: A review of the catalytic process through experimental and computational studies

In recent years, there has been significant interest from industrial and academic areas in the esterification of carboxylic acids catalyzed by acidic zeolites, as it represents a sustainable and economically viable approach to producing a wide range of high-value-added products. However, there is a lack of comprehensive reviews that address the intricate reaction mechanisms occurring at the catalyst interface at both the experimental and atomistic levels. Therefore, in this review, we provide an overview of the esterification reaction on acidic zeolites based on experimental and theoretical studies. The combination of infrared spectroscopy with atomistic calculations and experimental strategies using modulation excitation spectroscopy techniques combined with phase-sensitive detection is presented as an approach to detecting short-lived intermediates at the interface of zeolitic frameworks under realistic reaction conditions. To achieve this goal, this review has been divided into four sections: The first is a brief introduction highlighting the distinctive features of this review. The second addresses questions about the topology and activity of different zeolitic systems, since these properties are closely correlated in the esterification process. The third section deals with the mechanisms proposed in the literature. The fourth section presents advances in IR techniques and theoretical calculations that can be applied to gain new insights into reaction mechanisms. Finally, this review concludes with a subtle approach, highlighting the main aspects and perspectives of combining experimental and theoretical techniques to elucidate different reaction mechanisms in zeolitic systems.

Effect of Framework Composition and NH3 on the Diffusion of Cu+ in Cu-CHA Catalysts Predicted by Machine-Learning Accelerated Molecular Dynamics

Cu-exchanged zeolites rely on mobile solvated Cu+ cations for their catalytic activity, but the role of the framework composition in transport is not fully understood. Ab initio molecular dynamics simulations can provide quantitative atomistic insight but are too computationally expensive to explore large length and time scales or diverse compositions. We report a machine-learning interatomic potential that accurately reproduces ab initio results and effectively generalizes to allow multinanosecond simulations of large supercells and diverse chemical compositions. Biased and unbiased simulations of [Cu(NH3)2]+ mobility show that aluminum pairing in eight-membered rings accelerates local hopping and demonstrate that increased NH3 concentration enhances long-range diffusion. The probability of finding two [Cu(NH3)2]+ complexes in the same cage, which is key for SCR-NOx reaction, increases with Cu content and Al content but does not correlate with the long-range mobility of Cu+. Supporting experimental evidence was obtained from reactivity tests of Cu-CHA catalysts with a controlled chemical composition.

Intergrowth Zeolites, Synthesis, Characterization, and Catalysis

Microporous zeolites that can act as heterogeneous catalysts have continued to attract a great deal of academic and industrial interest, but current progress in their synthesis and application is restricted to single-phase zeolites, severely underestimating the potential of intergrowth frameworks. Compared with single-phase zeolites, intergrowth zeolites possess unique properties, such as different diffusion pathways and molecular confinement, or special crystalline pore environments for binding metal active sites. This review first focuses on the structural features and synthetic details of all the intergrowth zeolites, especially providing some insightful discussion of several potential frameworks. Subsequently, characterization methods for intergrowth zeolites are introduced, and highlighting fundamental features of these crystals. Then, the applications of intergrowth zeolites in several of the most active areas of catalysis are presented, including selective catalytic reduction of NOx by ammonia (NH3-SCR), methanol to olefins (MTO), petrochemicals and refining, fine chemicals production, and biomass conversion on Beta, and the relationship between structure and catalytic activity was profiled from the perspective of intergrowth grain boundary structure. Finally, the synthesis, characterization, and catalysis of intergrowth zeolites are summarized in a comprehensive discussion, and a brief outlook on the current challenges and future directions of intergrowth zeolites is indicated.

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.

Equation of state predictions for ScF3 and CaZrF6 with neural network-driven molecular dynamics

In silico property prediction based on density functional theory (DFT) is increasingly performed for crystalline materials. Whether quantitative agreement with experiment can be achieved with current methods is often an unresolved question, and may require detailed examination of physical effects such as electron correlation, reciprocal space sampling, phonon anharmonicity, and nuclear quantum effects (NQE), among others. In this work, we attempt first-principles equation of state prediction for the crystalline materials ScF3 and CaZrF6, which are known to exhibit negative thermal expansion (NTE) over a broad temperature range. We develop neural network (NN) potentials for both ScF3 and CaZrF6 trained to extensive DFT data, and conduct direct molecular dynamics prediction of the equation(s) of state over a broad temperature/pressure range. The NN potentials serve as surrogates of the DFT Hamiltonian with enhanced computational efficiency allowing for simulations with larger supercells and inclusion of NQE utilizing path integral approaches. The conclusion of the study is mixed: while some equation of state behavior is predicted in semiquantitative agreement with experiment, the pressure-induced softening phenomenon observed for ScF3 is not captured in our simulations. We show that NQE have a moderate effect on NTE at low temperature but does not significantly contribute to equation of state predictions at increasing temperature. Overall, while the NN potentials are valuable for property prediction of these NTE (and related) materials, we infer that a higher level of electron correlation, beyond the generalized gradient approximation density functional employed here, is necessary for achieving quantitative agreement with experiment.

Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics

Proton hopping is a key reactive process within zeolite catalysis. However, the accurate determination of its kinetics poses major challenges both for theoreticians and experimentalists. Nuclear quantum effects (NQEs) are known to influence the structure and dynamics of protons, but their rigorous inclusion through the path integral molecular dynamics (PIMD) formalism was so far beyond reach for zeolite catalyzed processes due to the excessive computational cost of evaluating all forces and energies at the Density Functional Theory (DFT) level. Herein, we overcome this limitation by training first a reactive machine learning potential (MLP) that can reproduce with high fidelity the DFT potential energy surface of proton hopping around the first Al coordination sphere in the H-CHA zeolite. The MLP offers an immense computational speedup, enabling us to derive accurate reaction kinetics beyond standard transition state theory for the proton hopping reaction. Overall, more than 0.6 μs of simulation time was needed, which is far beyond reach of any standard DFT approach. NQEs are found to significantly impact the proton hopping kinetics up to ~473 K. Moreover, PIMD simulations with deuterium can be performed without any additional training to compute kinetic isotope effects over a broad range of temperatures.

Use of Multiscale Data-Driven Surrogate Models for Flowsheet Simulation of an Industrial Zeolite Production Process

The production of catalysts such as zeolites is a complex multiscale and multi-step process. Various material properties, such as particle size or moisture content, as well as operating parameters—e.g., temperature or amount and composition of input material flows—significantly affect the outcome of each process step, and hence determine the properties of the final product. Therefore, the design and optimization of such processes is a complex task, which can be greatly facilitated with the help of numerical simulations. This contribution presents a modeling framework for the dynamic flowsheet simulation of a zeolite production sequence consisting of four stages: precipitation in a batch reactor; concentration and washing in a block of centrifuges; formation of droplets and drying in a spray dryer; and burning organic residues in a chain of rotary kilns. Various techniques and methods were used to develop the applied models. For the synthesis in the reactor, a multistage strategy was used, comprising discrete element method simulations, data-driven surrogate modeling, and population balance modeling. The concentration and washing stage consisted of several multicompartment decanter centrifuges alternating with water mixers. The drying is described by a co–current spray dryer model developed by applying a two-dimensional population balance approach. For the rotary kilns, a multi-compartment model was used, which describes the gas–solid reaction in the counter–current solids and gas flows.