Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery

Computational and Machine Learning Methods for CO2 Capture Using Metal-Organic Frameworks

Machine learning (ML) using data sets of atomic and molecular force fields (FFs) has made significant progress and provided benefits in the fields of chemistry and material science. This work examines the interactions between chemistry and materials computational science at the atomic and molecular scales for metal-organic framework (MOF) adsorbent development toward carbon dioxide (CO2) capture. Herein, a connection will be drawn between atomic forces predicted by ML algorithms and the structures of MOFs for CO2 adsorption. Our study also takes into account the successes of atomic computational screening in the field of materials science, especially quantum ML, and its relationship to ML algorithms that clarify advancements in the area of CO2 adsorption by MOFs. Additionally, we reviewed the processes for supplying data to ML algorithms for algorithm training, including text mining from scientific articles, and MOF's formula processing linked to the chemical properties of MOFs. To create ML algorithms for future research, we recommend that the digitization of scientific records can help efficiently synthesize advanced MOFs. Finally, a future vision for developing pioneer MOF synthesis routes for CO2 capture is presented in this review article.

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

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.

Design and Validation of a High-Throughput Reductive Catalytic Fractionation Method

Reductive catalytic fractionation (RCF) is a promising method to extract and depolymerize lignin from biomass, and bench-scale studies have enabled considerable progress in the past decade. RCF experiments are typically conducted in pressurized batch reactors with volumes ranging between 50 and 1000 mL, limiting the throughput of these experiments to one to six reactions per day for an individual researcher. Here, we report a high-throughput RCF (HTP-RCF) method in which batch RCF reactions are conducted in 1 mL wells machined directly into Hastelloy reactor plates. The plate reactors can seal high pressures produced by organic solvents by vertically stacking multiple reactor plates, leading to a compact and modular system capable of performing 240 reactions per experiment. Using this setup, we screened solvent mixtures and catalyst loadings for hydrogen-free RCF using 50 mg poplar and 0.5 mL reaction solvent. The system of 1:1 isopropanol/methanol showed optimal monomer yields and selectivity to 4-propyl substituted monomers, and validation reactions using 75 mL batch reactors produced identical monomer yields. To accommodate the low material loadings, we then developed a workup procedure for parallel filtration, washing, and drying of samples and a 1H nuclear magnetic resonance spectroscopy method to measure the RCF oil yield without performing liquid-liquid extraction. As a demonstration of this experimental pipeline, 50 unique switchgrass samples were screened in RCF reactions in the HTP-RCF system, revealing a wide range of monomer yields (21-36%), S/G ratios (0.41-0.93), and oil yields (40-75%). These results were successfully validated by repeating RCF reactions in 75 mL batch reactors for a subset of samples. We anticipate that this approach can be used to rapidly screen substrates, catalysts, and reaction conditions in high-pressure batch reactions with higher throughput than standard batch reactors.

The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture

Direct air capture (DAC) of CO2 with porous adsorbents such as metal-organic frameworks (MOFs) has the potential to aid large-scale decarbonization. Previous screening of MOFs for DAC relied on empirical force fields and ignored adsorbed H2O and MOF deformation. We performed quantum chemistry calculations overcoming these restrictions for thousands of MOFs. The resulting data enable efficient descriptions using machine learning.

The Current Understanding of Mechanistic Pathways in Zeolite Crystallization

Zeolite catalysts and adsorbents have been an integral part of many commercial processes and are projected to play a significant role in emerging technologies to address the changing energy and environmental landscapes. The ability to rationally design zeolites with tailored properties relies on a fundamental understanding of crystallization pathways to strategically manipulate processes of nucleation and growth. The complexity of zeolite growth media engenders a diversity of crystallization mechanisms that can manifest at different synthesis stages. In this review, we discuss the current understanding of classical and nonclassical pathways associated with the formation of (alumino)silicate zeolites. We begin with a brief overview of zeolite history and seminal advancements, followed by a comprehensive discussion of different classes of zeolite precursors with respect to their methods of assembly and physicochemical properties. The following two sections provide detailed discussions of nucleation and growth pathways wherein we emphasize general trends and highlight specific observations for select zeolite framework types. We then close with conclusions and future outlook to summarize key hypotheses, current knowledge gaps, and potential opportunities to guide zeolite synthesis toward a more exact science.

Perspectives on development of optoelectronic materials in artificial intelligence age

Optoelectronic devices, such as light-emitting diodes, have been demonstrated as one of the most demanded forthcoming display and lighting technologies because of their low cost, low power consumption, high brightness, and high contrast. The improvement of device performance relies on advances in precisely designing novelty functional materials, including light-emitting materials, hosts, hole/electron transport materials, and yet which is a time-consuming, laborious and resource-intensive task. Recently, machine learning (ML) has shown great prospects to accelerate material discovery and property enhancement. This review will summarize the workflow of ML in optoelectronic materials discovery, including data collection, feature engineering, model selection, model evaluation and model application. We highlight multiple recent applications of machine-learned potentials in various optoelectronic functional materials, ranging from semiconductor quantum dots (QDs) or perovskite QDs, organic molecules to carbon-based nanomaterials. We furthermore discuss the current challenges to fully realize the potential of ML-assisted materials design for optoelectronics applications. It is anticipated that this review will provide critical insights to inspire new exciting discoveries on ML-guided of high-performance optoelectronic devices with a combined effort from different disciplines.

Perspectives on Theoretical Models and Molecular Simulations of Polymer Brushes

Polymer brushes have witnessed extensive utilization and progress, driven by their distinct attributes in surface modification, tethered group functionality, and tailored interactions at the nanoscale, enabling them for various scientific and industrial applications of coatings, sensors, switchable/responsive materials, nanolithography, and lab-on-a-chips. Despite the wealth of experimental investigations into polymer brushes, this review primarily focuses on computational studies of antifouling polymer brushes with a strong emphasis on achieving a molecular-level understanding and structurally designing antifouling polymer brushes. Computational exploration covers three realms of thermotical models, molecular simulations, and machine-learning approaches to elucidate the intricate relationship between composition, structure, and properties concerning polymer brushes in the context of nanotribology, surface hydration, and packing conformation. Upon acknowledging the challenges currently faced, we extend our perspectives toward future research directions by delineating potential avenues and unexplored territories. Our overarching objective is to advance our foundational comprehension and practical utilization of polymer brushes for antifouling applications, leveraging the synergy between computational methods and materials design to drive innovation in this crucial field.

Engineering of increased L-Threonine production in bacteria by combinatorial cloning and machine learning

The goal of this study is to develop a general strategy for bacterial engineering using an integrated synthetic biology and machine learning (ML) approach. This strategy was developed in the context of increasing L-threonine production in Escherichia coli ATCC 21277. A set of 16 genes was initially selected based on metabolic pathway relevance to threonine biosynthesis and used for combinatorial cloning to construct a set of 385 strains to generate training data (i.e., a range of L-threonine titers linked to each of the specific gene combinations). Hybrid (regression/classification) deep learning (DL) models were developed and used to predict additional gene combinations in subsequent rounds of combinatorial cloning for increased L-threonine production based on the training data. As a result, E. coli strains built after just three rounds of iterative combinatorial cloning and model prediction generated higher L-threonine titers (from 2.7 g/L to 8.4 g/L) than those of patented L-threonine strains being used as controls (4-5 g/L). Interesting combinations of genes in L-threonine production included deletions of the tdh, metL, dapA, and dhaM genes as well as overexpression of the pntAB, ppc, and aspC genes. Mechanistic analysis of the metabolic system constraints for the best performing constructs offers ways to improve the models by adjusting weights for specific gene combinations. Graph theory analysis of pairwise gene modifications and corresponding levels of L-threonine production also suggests additional rules that can be incorporated into future ML models.

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.

Machine learning-assisted crystal engineering of a zeolite

It is shown that Machine Learning (ML) algorithms can usefully capture the effect of crystallization composition and conditions (inputs) on key microstructural characteristics (outputs) of faujasite type zeolites (structure types FAU, EMT, and their intergrowths), which are widely used zeolite catalysts and adsorbents. The utility of ML (in particular, Geometric Harmonics) toward learning input-output relationships of interest is demonstrated, and a comparison with Neural Networks and Gaussian Process Regression, as alternative approaches, is provided. Through ML, synthesis conditions were identified to enhance the Si/Al ratio of high purity FAU zeolite to the hitherto highest level (i.e., Si/Al = 3.5) achieved via direct (not seeded), and organic structure-directing-agent-free synthesis from sodium aluminosilicate sols. The analysis of the ML algorithms' results offers the insight that reduced Na₂O content is key to formulating FAU materials with high Si/Al ratio. An acid catalyst prepared by partial ion exchange of the high-Si/Al-ratio FAU (Si/Al = 3.5) exhibits improved proton reactivity (as well as specific activity, per unit mass of catalyst) in propane cracking and dehydrogenation compared to the catalyst prepared from the previously reported highest Si/Al ratio (Si/Al = 2.8).

Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics

Engineered materials are ubiquitous throughout society and are critical to the development of modern technology, yet many current material systems are inexorably tied to widespread deterioration of ecological processes. Next-generation material systems can address goals of environmental sustainability by providing alternatives to fossil fuel-based materials and by reducing destructive extraction processes, energy costs, and accumulation of solid waste. However, development of sustainable materials faces several key challenges including investigation, processing, and architecting of new feedstocks that are often relatively mechanically weak, complex, and difficult to characterize or standardize. In this review paper, we outline a framework for examining sustainability in material systems and discuss how recent developments in modeling, machine learning, and other computational tools can aid the discovery of novel sustainable materials. We consider these through the lens of materiomics, an approach that considers material systems holistically by incorporating perspectives of all relevant scales, beginning with first-principles approaches and extending through the macroscale to consider sustainable material design from the bottom-up. We follow with an examination of how computational methods are currently applied to select examples of sustainable material development, with particular emphasis on bioinspired and biobased materials, and conclude with perspectives on opportunities and open challenges.

Advanced zeolite and ordered mesoporous silica-based catalysts for the conversion of CO2 to chemicals and fuels

For many years, capturing, storing or sequestering CO₂ from concentrated emission sources or from air has been a powerful technique for reducing atmospheric CO₂. Moreover, the use of CO₂ as a C1 building block to mitigate CO₂ emissions and, at the same time, produce sustainable chemicals or fuels is a challenging and promising alternative to meet global demand for chemicals and energy. Hence, the chemical incorporation and conversion of CO₂ into valuable chemicals has received much attention in the last decade, since CO₂ is an abundant, inexpensive, nontoxic, nonflammable, and renewable one-carbon building block. Nevertheless, CO₂ is the most oxidized form of carbon, thermodynamically the most stable form and kinetically inert. Consequently, the chemical conversion of CO₂ requires highly reactive, rich-energy substrates, highly stable products to be formed or harder reaction conditions. The use of catalysts constitutes an important tool in the development of sustainable chemistry, since catalysts increase the rate of the reaction without modifying the overall standard Gibbs energy in the reaction. Therefore, special attention has been paid to catalysis, and in particular to heterogeneous catalysis because of its environmentally friendly and recyclable nature attributed to simple separation and recovery, as well as its applicability to continuous reactor operations. Focusing on heterogeneous catalysts, we decided to center on zeolite and ordered mesoporous materials due to their high thermal and chemical stability and versatility, which make them good candidates for the design and development of catalysts for CO₂ conversion. In the present review, we analyze the state of the art in the last 25 years and the potential opportunities for using zeolite and OMS (ordered mesoporous silica) based materials to convert CO₂ into valuable chemicals essential for our daily lives and fuels, and to pave the way towards reducing carbon footprint. In this review, we have compiled, to the best of our knowledge, the different reactions involving catalysts based on zeolites and OMS to convert CO₂ into cyclic and dialkyl carbonates, acyclic carbamates, 2-oxazolidones, carboxylic acids, methanol, dimethylether, methane, higher alcohols (C₂₊OH), C₂₊ (gasoline, olefins and aromatics), syngas (RWGS, dry reforming of methane and alcohols), olefins (oxidative dehydrogenation of alkanes) and simple fuels by photoreduction. The use of advanced zeolite and OMS-based materials, and the development of new processes and technologies should provide a new impulse to boost the conversion of CO₂ into chemicals and fuels.

Regulation of the Si/Al ratios and Al distributions of zeolites and their impact on properties

Zeolites are typically a class of crystalline microporous aluminosilicates that are constructed by SiO₄ and AlO₄ tetrahedra. Because of their unique porous structures, strong Brönsted acidity, molecular-level shape selectivity, exchangeable cations, and high thermal/hydrothermal stability, zeolites are widely used as catalysts, adsorbents, and ion-exchangers in industry. The activity, selectivity, and stability/durability of zeolites in applications are closely related to their Si/Al ratios and Al distributions in the framework. In this review, we discussed the basic principles and the state-of-the-art methodologies for regulating the Si/Al ratios and Al distributions of zeolites, including seed-assisted recipe modification, interzeolite transformation, fluoride media, and usage of organic structure-directing agents (OSDAs), etc. The conventional and newly developed characterization methods for determining the Si/Al ratios and Al distributions were summarized, which include X-ray fluorescence spectroscopy (XRF), solid state ²⁹Si/²⁷Al magic-angle-spinning nuclear magnetic resonance spectroscopy (²⁹Si/²⁷Al MAS NMR), Fourier-transform infrared spectroscopy (FT-IR), etc. The impact of Si/Al ratios and Al distributions on the catalysis, adsorption/separation, and ion-exchange performance of zeolites were subsequently demonstrated. Finally, we presented a perspective on the precise control of the Si/Al ratios and Al distributions of zeolites and the corresponding challenges.

Quantitative high-throughput measurement of bulk mechanical properties using commonly available equipment

Machine learning approaches have introduced an urgent need for large datasets of materials properties. However, for mechanical properties, current high-throughput measurement methods typically require complex robotic instrumentation, with enormous capital costs that are inaccessible to most experimentalists. A quantitative high-throughput method using only common lab equipment and consumables with simple fabrication steps is long desired. Here, we present such a technique that can measure bulk mechanical properties in soft materials with a common laboratory centrifuge, multiwell plates, and microparticles. By applying a homogeneous force on the particles embedded inside samples in the multiwell plate using centrifugation, we show that our technique measures the fracture stress of gels with similar accuracy to a rheometer. However, our method has a throughput on the order of 10³ samples per run and is generalizable to virtually all soft material systems. We hope that our method can expedite materials discovery and potentially inspire the future development of additional high-throughput characterization methods.

Ranking the synthesizability of hypothetical zeolites with the sorting hat

Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-haystack problem of finding promising candidates among large databases of predicted structures has intrigued materials scientists for decades; yet, most work to date on the zeolite problem has been limited to intuitive structural descriptors. Here, we tackle this problem through a rigorous data science scheme-the "Zeolite Sorting Hat"-that exploits interatomic correlations to discriminate between real and hypothetical zeolites and to partition real zeolites into compositional classes that guide synthetic strategies for a given hypothetical framework. We find that, regardless of the structural descriptor used by the Zeolite Sorting Hat, there remain hypothetical frameworks that are incorrectly classified as real ones, suggesting that they might be good candidates for synthesis. We seek to minimize the number of such misclassified frameworks by using as complete a structural descriptor as possible, thus focusing on truly viable synthetic targets, while discovering structural features that distinguish real and hypothetical frameworks as an output of the Zeolite Sorting Hat. Further ranking of the candidates can be achieved based on thermodynamic stability and/or their suitability for the desired applications. Based on this workflow, we propose three hypothetical frameworks differing in their molar volume range as the top targets for synthesis, each with a composition suggested by the Zeolite Sorting Hat. Finally, we analyze the behavior of the Zeolite Sorting Hat with a hierarchy of structural descriptors including intuitive descriptors reported in previous studies, finding that intuitive descriptors produce significantly more misclassified hypothetical frameworks, and that more rigorous interatomic correlations point to second-neighbor Si-O distances around 3.2-3.4 Å as the key discriminatory factor.

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture

Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.

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.

Optimal machine learning feature selection for assessing the mechanical properties of a zeolite framework

In this study, 45 and 249 critical features were discovered among 896 zeolite descriptors generated by the matminer package for estimating the shear and bulk moduli of zeolites, respectively. A database containing the mechanical properties of 873 zeolite structures, calculated using density functional theory, was used to train the machine learning regression model. The results of using these critical features with the LightGBM algorithm were rigorously compared with those from other regressors as well as with different sets of features. The comparison results indicate that the surrogate model with critical features increases the prediction accuracy of the bulk and shear moduli of zeolites by 17.3% and 10.6%, respectively, and reduces the prediction uncertainty by one-third of that achieved using previously available features. The suggested features originating from several physical and chemical groups highlight the unveiled relationships between the features and mechanical properties of zeolites. The robustness of the constructed model with 356 features was confirmed by applying a set of different training-test set ratios. We believe that the suggested critical features of zeolites can help to understand the mechanical behavior of a half million unlabeled hypothetical zeolite structures and accelerate the discovery of novel zeolites with unprecedented mechanical properties.

Tunable CHA/AEI Zeolite Intergrowths with A Priori Biselective Organic Structure-Directing Agents: Controlling Enrichment and Implications for Selective Catalytic Reduction of NOx

A novel ab initio methodology based on high‐throughput simulations has permitted designing unique biselective organic structure‐directing agents (OSDAs) that allow the efficient synthesis of CHA/AEI zeolite intergrowth materials with controlled phase compositions. Distinctive local crystallographic ordering of the CHA/AEI intergrowths was revealed at the nanoscale level using integrated differential phase contrast scanning transmission electron microscopy (iDPC STEM). These novel CHA/AEI materials have been tested for the selective catalytic reduction (SCR) of NOx, presenting an outstanding catalytic performance and hydrothermal stability, even surpassing the performance of the well‐established commercial CHA‐type catalyst. This methodology opens the possibility for synthetizing new zeolite intergrowths with more complex structures and unique catalytic properties.

Carbocation chemistry confined in zeolites: spectroscopic and theoretical characterizations

Acid-catalyzed reactions inside zeolites are one type of broadly applied industrial reactions, where carbocations are the most common intermediates of these reaction processes, including methanol to olefins, alkene/aromatic alkylation, and hydrocarbon cracking/isomerization. The fundamental research on these acid-catalyzed reactions is focused on the stability, evolution, and lifetime of carbocations under the zeolite confinement effect, which greatly affects the efficiency, selectivity and deactivation of zeolite catalysts. Therefore, a profound understanding of the carbocations confined in zeolites is not only beneficial to explain the reaction mechanism but also drive the design of new zeolite catalysts with ideal acidity and cages/channels. In this review, we provide both an in-depth understanding of the stabilization of carbocations by the pore confinement effect and summary of the advanced characterization methods to capture carbocations in zeolites, including UV-vis spectroscopy, solid-state NMR, fluorescence microscopy, IR spectroscopy and Raman spectroscopy. Also, we clarify the relationship between the activity and stability of carbocations in zeolite-catalyzed reactions, and further highlight the role of carbocations in various hydrocarbon conversion reactions inside zeolites with diverse frameworks and varying acidic properties.

Unraveling templated-regulated distribution of isolated SiO4 tetrahedra in silicoaluminophosphate zeolites with high-throughput computations

Silicoaluminophosphate (SAPO) zeolites are well-known catalytic materials because of the mild acidity originating from the isolated SiO₄ tetrahedra in their frameworks. Regulating the distribution of isolated SiO₄ tetrahedra in SAPO zeolites is formidably challenging because SiO₄ tetrahedra tend to agglomerate to form Si islands and the isolated SiO₄ tetrahedra are difficult to determine using conventional characterization techniques. Here we synthesized Si-island-free SAPO-35 zeolites by using N-methylpiperidine as a new template, which exhibited excellent thermal stability compared to conventional SAPO-35 zeolites and a substantially improved methanol-to-olefins catalytic lifetime even comparable to that of commercial SAPO-34 zeolites. More strikingly, with the aid of high-throughput computations on 44 697 structure models combined with various state-of-the-art characterization techniques, for the first time, we reveal that the host–guest interactions between template molecules and SAPO frameworks determine the specific distributions of isolated SiO₄ tetrahedra, which are responsible for the improvement in the chemical properties of zeolites. Our work provides an insight into the template-based regulation of isolated SiO₄ tetrahedra in SAPO zeolites, which opens a new avenue in the discovery of promising zeolite catalysts with optimal SiO₄ distribution.

Machine learning potential era of zeolite simulation

Zeolites, owing to their great variety and complexity in structure and wide applications in chemistry, have long been the hot topic in chemical research. This perspective first presents a short retrospect of theoretical investigations on zeolites using the tools from classical force fields to quantum mechanics calculations and to the latest machine learning (ML) potential simulations. ML potentials as the next-generation technique for atomic simulation open new avenues to simulate and interpret zeolite systems and thus hold great promise for finally predicting the structure-functionality relation of zeolites. Recent advances using ML potentials are then summarized from two main aspects: the origin of zeolite stability and the mechanism of zeolite-related catalytic reactions. We also discussed the possible scenarios of ML potential application aiming to provide instantaneous and easy access of zeolite properties. These advanced applications could now be accomplished by combining cloud-computing-based techniques with ML potential-based atomic simulations. The future development of ML potentials for zeolites in the respects of improving the calculation accuracy, expanding the application scope and constructing the zeolite-related datasets is finally outlooked.

Machine learning accelerated high-throughput screening of zeolites for the selective adsorption of xylene isomers

A combination of machine learning and high throughput simulation has identified several potential zeolite structures that appear to outperform the leading commercially used material and explained the key factors for high selectivity.

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.

Carbon dioxide capture with zeotype materials

This review describes the application of zeotype materials for the capture of CO2 in different scenarios, the critical parameters defining the adsorption performances, and the challenges of zeolitic adsorbents for CO2 capture.

Construction of cucurbit[n]uril-based supramolecular frameworks via host-guest inclusion and functional properties thereof

Frameworks utilizing cucurbit[n]uril-based chemistry build on the rapid developments in the fields of metal-organic frameworks (MOFs), covalent-organic frameworks (COFs), and supramolecular organic frameworks (SOFs), and as porous materials have found...

Controlling Nucleation Pathways in Zeolite Crystallization: Seeding Conceptual Methodologies for Advanced Materials Design

A core objective of synthesizing zeolites for widespread applications is to produce materials with properties and corresponding performances that exceed conventional counterparts. This places an impetus on elucidating and controlling processes of crystallization where one of the most critical design criteria is the ability to prepare zeolite crystals with ultrasmall dimensions to mitigate the deleterious effects of mass transport limitations. At the most fundamental level, this requires a comprehensive understanding of nucleation to address this ubiquitous materials gap. This Perspective highlights recent methodologies to alter zeolite nucleation by using seed-assisted protocols and the exploitation of interzeolite transformations to design advanced materials. Introduction of crystalline seeds in complex growth media used to synthesize zeolites can have wide-ranging effects on the physicochemical properties of the final product. Here we discuss the diverse pathways of zeolite nucleation, recent breakthroughs in seed-assisted syntheses of nanosized and hierarchical materials, and shortcomings for developing generalized guidelines to predict synthesis outcomes. We offer a critical analysis of state-of-the-art approaches to tailor zeolite crystallization wherein we conceptualize whether parallels between network theory and zeolite synthesis can be instrumental for translating key findings of individual discoveries across a broader set of zeolite crystal structures and/or synthesis conditions.

Monte carlo simulations and experiments of all-silica zeolite LTA assembly combining structure directing agents that match cage sizes

We investigated the influence of organic structure-directing agents (OSDAs) on the formation rates of all-silica zeolite LTA using both simulations and experiments, to shed light on the crystallization process. We compared syntheses using one OSDA with a diameter close to the size of the large cavity in LTA, and two OSDAs of diameters matching the sizes of both the small and large LTA cavities. Reaction-ensemble Monte Carlo (RxMC) simulations predict a speed up of LTA formation using two OSDAs matching the LTA pore sizes; this qualitative result is confirmed by experimental studies of crystallization kinetics, which find a speedup in all-silica LTA crystallization of a factor of 3. Analyses of simulated rings and their Si-O-Si angular energies during RxMC crystallizations show that all ring sizes in the faster crystallization exhibit lower angular energies, on average, than in the slower crystallization, explaining the origin of the speedup through packing effects.

A priori control of zeolite phase competition and intergrowth with high-throughput simulations

[Figure: see text].

Thermodynamic exploration of xenon/krypton separation based on a high-throughput screening

Nanoporous framework materials are a promising class of materials for energy-efficient technology of xenon/krypton separation by physisorption. Many studies on Xe/Kr separation by adsorption have focused on the determination of structure/property relationships, the description of theoretical limits of performance, and the identification of top-performing materials. Here, we provide a study based on a high-throughput screening of the adsorption of Xe, Kr, and Xe/Kr mixtures in 12 020 experimental MOF materials, to provide a better comprehension of the thermodynamics behind Xe/Kr separation in nanoporous materials and the microscopic origins of Xe/Kr selectivity at both low and ambient pressure.

Activation and conversion of alkanes in the confined space of zeolite-type materials

Microporous zeolite-type materials, with crystalline porous structures formed by well-defined channels and cages of molecular dimensions, have been widely employed as heterogeneous catalysts since the early 1960s, due to their wide variety of framework topologies, compositional flexibility and hydrothermal stability. The possible selection of the microporous structure and of the elements located in framework and extraframework positions enables the design of highly selective catalysts with well-defined active sites of acidic, basic or redox character, opening the path to their application in a wide range of catalytic processes. This versatility and high catalytic efficiency is the key factor enabling their use in the activation and conversion of different alkanes, ranging from methane to long chain n-paraffins. Alkanes are highly stable molecules, but their abundance and low cost have been two main driving forces for the development of processes directed to their upgrading over the last 50 years. However, the availability of advanced characterization tools combined with molecular modelling has enabled a more fundamental approach to the activation and conversion of alkanes, with most of the recent research being focused on the functionalization of methane and light alkanes, where their selective transformation at reasonable conversions remains, even nowadays, an important challenge. In this review, we will cover the use of microporous zeolite-type materials as components of mono- and bifunctional catalysts in the catalytic activation and conversion of C₁⁺ alkanes under non-oxidative or oxidative conditions. In each case, the alkane activation will be approached from a fundamental perspective, with the aim of understanding, at the molecular level, the role of the active sites involved in the activation and transformation of the different molecules and the contribution of shape-selective or confinement effects imposed by the microporous structure.

Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks

Organic structure directing agents (OSDAs) play a crucial role in the synthesis of micro- and mesoporous materials especially in the case of zeolites. Despite the wide use of OSDAs, their interaction with zeolite frameworks is poorly understood, with researchers relying on synthesis heuristics or computationally expensive techniques to predict whether an organic molecule can act as an OSDA for a certain zeolite. In this paper, we undertake a data-driven approach to unearth generalized OSDA-zeolite relationships using a comprehensive database comprising of 5,663 synthesis routes for porous materials. To generate this comprehensive database, we use natural language processing and text mining techniques to extract OSDAs, zeolite phases, and gel chemistry from the scientific literature published between 1966 and 2020. Through structural featurization of the OSDAs using weighted holistic invariant molecular (WHIM) descriptors, we relate OSDAs described in the literature to different types of cage-based, small-pore zeolites. Lastly, we adapt a generative neural network capable of suggesting new molecules as potential OSDAs for a given zeolite structure and gel chemistry. We apply this model to CHA and SFW zeolites generating several alternative OSDA candidates to those currently used in practice. These molecules are further vetted with molecular mechanics simulations to show the model generates physically meaningful predictions. Our model can automatically explore the OSDA space, reducing the amount of simulation or experimentation needed to find new OSDA candidates.

Accelerated Discovery of Zeolite Structures with Superior Mechanical Properties via Active Learning

A Bayesian active learning platform is developed for the accelerated discovery of mechanically superior zeolite structures from more than half a million hypothetical candidates. An initial database containing the mechanical properties of synthesizable zeolites is trained to develop the machine learning regression model. Then, a Bayesian optimization scheme is implemented to identify zeolites with potentially excellent mechanical properties. The newly accumulated database consists of 871 labeled structures, and the uncertainty of the predictive model is reduced by 40% and 58% for the bulk and shear moduli, respectively. The model convergence shows that no further improvement occurs after the 10th iteration of optimizations. The proposed platform is able to discover 23 new zeolite structures that have unprecedented shear moduli; in one case, the shear modulus (127.81 GPa) is 250% higher than the previous data set. The proposed platform accelerates the material discovery process while maximizing computational efficiency and enhancing the predictive accuracy.

Machine Learning-Enabled Design and Prediction of Protein Resistance on Self-Assembled Monolayers and Beyond

The rational design of highly antifouling materials is crucial for a wide range of fundamental research and practical applications. The immense variety and complexity of the intrinsic physicochemical properties of materials (i.e., chemical structure, hydrophobicity, charge distribution, and molecular weight) and their surface coating properties (i.e., packing density, film thickness and roughness, and chain conformation) make it challenging to rationally design antifouling materials and reveal their fundamental structure-property relationships. In this work, we developed a data-driven machine learning model, a combination of factor analysis of functional group (FAFG), Pearson analysis, random forest (RF) and artificial neural network (ANN) algorithms, and Bayesian statistics, to computationally extract structure/chemical/surface features in correlation with the antifouling activity of self-assembled monolayers (SAMs) from a self-construction data set. The resultant model demonstrates the robustness of Q_CV² = 0.90 and RMSE_CV = 0.21 and the predictive ability of Q_ext² = 0.84 and RMSE_ext = 0.28, determines key descriptors and functional groups important for the antifouling activity, and enables to design original antifouling SAMs using the predicted antifouling functional groups. Three computationally designed molecules were further coated onto the surfaces in different forms of SAMs and polymer brushes. The resultant coatings with negative fouling indexes exhibited strong surface resistance to protein adsorption from undiluted blood serum and plasma, validating the model predictions. The data-driven machine learning model demonstrates their design and predictive capacity for next-generation antifouling materials and surfaces, which hopefully help to accelerate the discovery and understanding of functional materials.