Machine-learning approach to the design of OSDAs for zeolite beta

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.

Structure-Energy Relationship Prediction of the HZSM-5 Zeolite with Different Acid Site Distributions by the Neural Network Model

Zeolites are a very important family of catalysts. The catalytic activity of zeolites depends on the distribution of acid sites, which has been extensively studied. However, the relationship between the acid site distribution and catalytic efficiency remains unestablished. An onerous computational burden can be imposed when static calculations are applied to analyze the relationship between a catalyst structure and its energy. To resolve this issue, the current work uses neural network (NN) models to evaluate the relationship. By taking the typical HZSM-5 zeolite as an example, we applied the provided atomic coordinates to predict the energy. The network performances of the artificial neural network (ANN) and high-dimensional neural network (HDNN) are compared using the trained results from a dataset containing the identical number of acid sites. Furthermore, the importance of the feature is examined with the aid of a random forest model to identify the pivotal variables influencing the energy. In addition, the HDNN is employed to forecast the energy of an HZSM-5 system with varying numbers of acid sites. This study emphasizes that the energy of zeolites can be rapidly and accurately predicted through the NN, which can assist our understanding of the relationship between the structure and properties, thereby providing more accurate and efficient methods for the application of zeolite materials.

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.

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.

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.

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.

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

[Figure: see text].

Computational elucidation of the aging time effect on zeolite synthesis selectivity in the presence of water and diquaternary ammonium iodide

An example of zeolite selectivity (MFI → MOR) driven by synthesis aging time has been studied. Using N,N,N',N'-tetramethyl-N,N'-dipropyl-ethylenediammonium diiodide (TMDP) as an organic structure-directing agent (OSDA), the zeolite phases obtained at 2 h (MFI 97%), 8 h (MFI 84%, MOR 16%) and 24 h (MFI 43%, MOR 57%) have been characterized by powder X-ray diffraction. The results suggest that at intermediate aging time, namely 8 h and 24 h, the dominant phase (MFI) is displaced by MOR. Different techniques (FT-IR, Raman, ¹³C MAS NMR, TGA/DTG and HC microanalysis) have been employed to verify the OSDA integrity and occlusion inside the zeolite micropores as well as to quantify the water and OSDA loading. The ¹H MAS NMR of the as-made occluded zeolite was compared with the spectra of TMDP and the recovered OSDA from the sample by extraction with water. The comparison indicated that TMDP was not structurally intact, indicating the chemical transformation of TMDP to imidazolinium homologues through the Hofmann degradation process. Furthermore, careful acidic breakdown of the aluminosilicate shell, covered on the zeolite samples by hydrofluoric acid, revealed that the remaining OSDA had been partially degraded to lower molecular weight ammonium salt, confirmed by ¹H NMR and mass spectrometry measurements. A computational study was performed by using a force field based methodology, including accurate loading of water and OSDA in the zeolite (MFI and MOR) unit cells. The results show an important contribution of the presence of water. The samples with larger aging time (8 h and 24 h) incorporate less water and show partial TMDP degradation, whilst at the shortest aging time (2 h), there is a larger water content and TMDP remains intact. The larger accessible volume of MFI justifies that this is the dominant phase at short aging times (large water content) since it can accommodate a larger number of water molecules than MOR. The OSDA partial degradation also plays a role. At longer aging times the partial OSDA decomposition has been considered in the models by including TMDP + Imidaz, which is more stabilized by MOR, whilst at shorter aging times the only OSDA present, TMDP, is better stabilized by MFI.

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.

Benchmarking binding energy calculations for organic structure-directing agents in pure-silica zeolites

Molecular modeling plays an important role in the discovery of organic structure-directing agents (OSDAs) for zeolites. By quantifying the intensity of host-guest interactions, it is possible to select cost-effective molecules that maximize binding toward a given zeolite framework. Over the last few decades, a variety of methods and levels of theory have been used to calculate these binding energies. Nevertheless, there is no consensus on the best calculation strategy for high-throughput virtual screening undertakings. In this work, we compare binding affinities from density functional theory (DFT) and Dreiding force field calculations for 272 zeolite-OSDA pairs obtained from static and time-averaged simulations. Enabled by automation software, we show that Dreiding binding energies from the frozen pose method correlate best with DFT energies. They are also less sensitive to the choice of initial lattice parameters and optimization algorithms, as well as less computationally expensive than their time-averaged counterparts. Furthermore, we demonstrate that a broader exploration of the conformation space from molecular dynamics simulations does not provide significant improvements in binding energy trends over the frozen pose method despite being orders of magnitude more expensive. The code and benchmark data are open-sourced and provide robust and computationally efficient guidelines to calculating binding energies in zeolite-OSDA pairs.

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.

Structure-Directing Roles of Organic Molecules in the Formation of Aluminosilicate and Aluminophosphate Molecular Sieves Revealed by 2D 1 H DQ-SQ NMR Spectroscopy

Understanding of crystallization mechanisms of molecular sieves is driven by the broad range of usefulness and unique properties they possess. It is still difficult to obtain information related to the crystallization mechanism of molecular sieves, partly because the materials are generally prepared under hydrothermal conditions and the whole reaction happens in the "black box" autoclave. In this work, 2D ¹ H DQ-SQ NMR results clearly demonstrate that it is not only the electrostatic interactions between organic structure-directing agents (OSDAs) and the framework, but also the correlation among OSDAs playing the dominant structural directing roles during the crystallization process. Our fundamental understanding of the crystallization mechanism of molecular sieves could be of great value to design and synthesize new molecular sieves with desirable structural properties.

Data science assisted investigation of catalytically active copper hydrate in zeolites for direct oxidation of methane to methanol using H2O2

Dozens of Cu zeolites with MOR, FAU, BEA, FER, CHA and MFI frameworks are tested for direct oxidation of CH₄ to CH₃OH using H₂O₂ as oxidant. To investigate the active structures of the Cu zeolites, 15 structural variables, which describe the features of the zeolite framework and reflect the composition, the surface area and the local structure of the Cu zeolite active site, are collected from the Database of Zeolite Structures of the International Zeolite Association (IZA). Also analytical studies based on inductively coupled plasma-optical emission spectrometry (ICP-OES), X-ray fluorescence (XRF), N₂ adsorption specific surface area measurement and X-ray absorption fine structure (XAFS) spectral measurement are performed. The relationships between catalytic activity and the structural variables are subsequently revealed by data science techniques, specifically, classification using unsupervised and supervised machine learning and data visualization using pairwise correlation. Based on the unveiled relationships and a detailed analysis of the XAFS spectra, the local structures of the Cu zeolites with high activity are proposed.

Elucidating the Role of Tetraethylammonium in the Silicate Condensation Reaction from Ab Initio Molecular Dynamics Simulations

The understanding of the formation of silicate oligomers in the initial stage of zeolite synthesis is important. The use of organic structure-directing agents (OSDAs) is known to be a key factor in the formation of different silicate species and the final zeolite structure. For example, tetraethylammonium ion (TEA⁺) is a commonly used organic template for zeolite synthesis. In this study, ab initio molecular dynamics (AIMD) simulation is used to provide an understanding of the role of TEA⁺ in the formation of various silicate oligomers, ranging from dimer to 4-ring. Calculated free-energy profiles of the reaction pathways show that the formation of a 4-ring structure has the highest energy barrier (97 kJ/mol). The formation of smaller oligomers such as dimer, trimer, and 3-ring has lower activation barriers. The TEA⁺ ion plays an important role in regulating the predominant species in solution via its coordination with silicate structures during the condensation process. The kinetics and thermodynamics of the oligomerization reaction indicate a more favorable formation of the 3-ring over the 4-ring structure. The results from AIMD simulations are in line with the experimental observation that TEA⁺ favors the 3-ring and double 3-ring in solution. The results of this study imply that the role of OSDAs is not only important for the host-guest interaction but also crucial for controlling the reactivity of different silicate oligomers during the initial stage of zeolite formation.

The Role of Machine Learning in the Understanding and Design of Materials

Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which can have huge technological and social impact. However, such rational design requires a holistic perspective over the full multistage design process, which involves exploring immense materials spaces, their properties, and process design and engineering as well as a techno-economic assessment. The complexity of exploring all of these options using conventional scientific approaches seems intractable. Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design. Here we review some of the chief advancements of these methods and their applications in rational materials design, followed by a discussion on some of the main challenges and opportunities we currently face together with our perspective on the future of rational materials design and discovery.

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

Porous materials are widely employed in a large range of applications, in particular, for storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre- and post-synthetic computer simulations are mostly carried out in a piecemeal and ad hoc manner. Whilst high throughput approaches have been used for more than 30 years in the porous material fields, routine integration of experimental and computational processes is only now becoming more established. Herein, important developments are highlighted and emerging challenges for the community identified, including the need to work toward more integrated workflows.

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal-organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. Given the increasing interest of the scientific community in machine learning, we expect this list to rapidly expand in the coming years.

Synthesis and Post-Synthesis Transformation of Germanosilicate Zeolites

Zeolites are one of the most important heterogeneous catalysts, with a high number of large-scale industrial applications. While the synthesis of new zeolites remain rather limited, introduction of germanium has substantially increased our ability to not only direct the synthesis of zeolites but also to convert them into new materials post-synthetically. The smaller Ge-O-Ge angles (vs. Si-O-Si) and lability of the Ge-O bonds in aqueous solutions account for this behaviour. This Minireview discusses critical aspects of germanosilicate synthesis and their post-synthesis transformations to porous materials.

Computational Screening of Structure-Directing Agents for the Synthesis of Pure Silica ITE Zeolite

"Shape" was the first criterion claimed to explain the specificity between organic structure-directing agents (OSDAs) and zeolite micropores. With the advent of computational chemistry methods applied to study the effectiveness of SDA-zeolite combinations, "energy" (mainly van der Waals) became the most commonly invoked concept to explain the zeolite phase selectivity. The lower the energy, the better the SDA. In this study, we rescue the concept of shape, and we combine it with the concept of energy within the frame of a SDA screening approach to identify new SDAs for the synthesis of cage-based ITE zeolite. Once we identify an appropriate shape fingerprint, filtering through the SDA database can be done quickly and accurately. With the shape selection, an automated Monte Carlo software allows us to assess the suitability using the force-field-calculated zeo-SDA energy. The computational approach can be promptly applied to other cage-based zeolites.

Multi-objective de novo molecular design of organic structure-directing agents for zeolites using nature-inspired ant colony optimization

Organic structure-directing agents (OSDAs) are often employed for synthesis of zeolites with desired frameworks. A priori prediction of such OSDAs has mainly relied on the interaction energies between OSDAs and zeolite frameworks, without cost considerations. For practical purposes, the cost of OSDAs becomes a critical issue. Therefore, the development of a computational de novo prediction methodology that can speed up the trial-and-error cycle in the search for less expensive OSDAs is desired. This study utilized a nature-inspired ant colony optimization method to predict physicochemically and/or economically preferable OSDAs, while also taking molecular similarity and heuristics of zeolite synthesis into consideration. The prediction results included experimentally known OSDAs, candidates having structures closely related to known OSDAs, and novel ones, suggesting the applicability of this approach.

Inspired by the exploratory methods of ant colonies, adaptive optimization was employed to explore the chemical space for organic molecules that guide zeolite crystallization, giving both physicochemically and economically promising molecules.

Identification of New Templates for the Synthesis of BEA, BEC, and ISV Zeolites Using Molecular Topology and Monte Carlo Techniques

The presence of organic structure directing agents (templates) in the synthesis of zeolites allows the synthesis to be directed, in many cases, toward structures in which there is a large stabilization between the template and the zeolite micropore due to dispersion interactions. Although other factors are also important (temperature, pH, Si/Al ratio, etc.), systems with strong zeolite-template interactions are good candidates for an application of new computational algorithms, for instance those based in molecular topology (MT), that can be used in combination with large databases of organic molecules. Computational design of new templates allows the synthesis of existing and new zeolites to be expanded and refined. Three zeolites with similar 3-D large pore systems, BEA, BEC, and ISV, were selected with the aim of finding new templates for their selective syntheses. Using a training set of active and inactive templates (obtained from the literature) for the synthesis of target zeolites, it was possible to select chemical descriptors related to activity, meaning a good candidate template. With a discriminant function defined upon MT, the screening through a database of organic molecules led to a small subset (preselection) of candidate templates for the synthesis of BEA, BEC, and ISV. As far as we know, this is the first time that topological/topochemical descriptors, which do not consider 3-D information on the molecules, have been used to predict the activity of zeolite structure directing agents (SDAs). Following the prediction of SDAs using MT, an automated approach of sequential template filling of micropores based on a combination of Monte Carlo and lattice energy minimization was applied for all the candidate templates in the three zeolites. Two results can be obtained from this: an evaluation of the quality of the molecular topology QSAR models leading to the preselection of templates, and a final selection of candidate templates for the selective synthesis of BEA, BEC, and ISV. Regarding the latter, a good template will be that which maximizes the zeolite-template dispersion interactions with one, and only one, of the three zeolites. The presented methodology can be used to find alternative (maybe cheaper or perhaps more selective) templates than those already known.

Machine learning for target discovery in drug development

The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically manipulated cell lines or chemical proteomics is pursued to shed light on their biology and deconvolute drug-target networks. By taking advantage of the ever-growing wealth of publicly available bioactivity data, learning algorithms now provide an attractive means to generate statistically motivated research hypotheses and thereby prioritize biochemical screens. Here, we highlight recent successes in machine intelligence for target identification and discuss challenges and opportunities for drug discovery.

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

Zeolites are microporous crystalline materials with well-defined cavities and pores, which can be prepared under different pore topologies and chemical compositions. Their preparation is typically defined by multiple interconnected variables (e.g., reagent sources, molar ratios, aging treatments, reaction time and temperature, among others), but unfortunately their distinctive influence, particularly on the nucleation and crystallization processes, is still far from being understood. Thus, the discovery and/or optimization of specific zeolites is closely related to the exploration of the parametric space through trial-and-error methods, generally by studying the influence of each parameter individually. In the past decade, machine learning (ML) methods have rapidly evolved to address complex problems involving highly nonlinear or massively combinatorial processes that conventional approaches cannot solve. Considering the vast and interconnected multiparametric space in zeolite synthesis, coupled with our poor understanding of the mechanisms involved in their nucleation and crystallization, the use of ML is especially timely for improving zeolite synthesis. Indeed, the complex space of zeolite synthesis requires drawing inferences from incomplete and imperfect information, for which ML methods are very well-suited to replace the intuition-based approaches traditionally used to guide experimentation. In this Account, we contend that both existing and new ML approaches can provide the "missing link" needed to complete the traditional zeolite synthesis workflow used in our quest to rationalize zeolite synthesis. Within this context, we have made important efforts on developing ML tools in different critical areas, such as (1) data-mining tools to process the large amount of data generated using high-throughput platforms; (2) novel complex algorithms to predict the formation of energetically stable hypothetical zeolites and guide the synthesis of new zeolite structures; (3) new "ab initio" organic structure directing agent predictions to direct the synthesis of hypothetical or known zeolites; (4) an automated tool for nonsupervised data extraction and classification from published research articles. ML has already revolutionized many areas in materials science by enhancing our ability to map intricate behavior to process variables, especially in the absence of well-understood mechanisms. Undoubtedly, ML is a burgeoning field with many future opportunities for further breakthroughs to advance the design of molecular sieves. For this reason, this Account includes an outlook of future research directions based on current challenges and opportunities. We envision this Account will become a hallmark reference for both well-established and new researchers in the field of zeolite synthesis.