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

[EN] CONSPECTUS: 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.This work has been supported by the EU through ERC-AdG2014-671093, by the Spanish Government through SEV-20160683 and RTI2018-101033-B-I00 (MCIU/AEI/FEDER, UE), and by La Caixa-Foundation through MIT -SPAIN MISTI program (LCF/PR/MIT17/11820002). Y.R.-L. thanks the DoE for funding through the Office of Basic Energy Sciences (DE-SC0016214).Moliner Marin, M.; Román-Leshkov, Y.; Corma Canós, A. (2019). Machine Learning Applied to Zeolite Synthesis: The Missing Link for Realizing High-Throughput Discovery. Accounts of Chemical Research. 52(10):2971-2980. https://doi.org/10.1021/acs.accounts.9b00399S29712980521

Biomass Gasification and Applied Intelligent Retrieval in Modeling

Gasification technology often requires the use of modeling approaches to incorporate several intermediate reactions in a complex nature. These traditional models are occasionally impractical and often challenging to bring reliable relations between performing parameters. Hence, this study outlined the solutions to overcome the challenges in modeling approaches. The use of machine learning (ML) methods is essential and a promising integration to add intelligent retrieval to traditional modeling approaches of gasification technology. Regarding this, this study charted applied ML-based artificial intelligence in the field of gasification research. This study includes a summary of applied ML algorithms, including neural network, support vector, decision tree, random forest, and gradient boosting, and their performance evaluations for gasification technologies

ZnO/Mg-Al Layered Double Hydroxides as a Photocatalytic Bleaching of Methylene Orange - A Black Box Modeling by Artificial Neural Network

The paper reports the development of ZnO-MgAl layered double hydroxides as an adsorbent-photo catalyst to remove the dye pollutants from aqueous solution and the experiments of a photocatalytic study were designed and modeled by response surface methodology (RSM) and artificial neural network (ANN). The co-precipitation and urea methods were used to synthesize the ZnO-MgAl layered double hydroxides and FT-IR, XRD and SEM analysis were done for characterization of the catalyst.The performance of the ANN model was determined and showed the efficiency of the model in comparison to the RSM method to predict the percentage of dye removal accurately with a determination coefficient (R2) of 0.968. The optimized conditions were obtained as follows: 600 oC, 120 min, 0.05 g and 20 ppm for the calcination temperature, irradiation time, catalyst amount and dye pollutant concentration, respectively.

Novel Single-Site Titanosilicates with Targeted Connectivity and Nuclearity of Titanium(IV): Synthesis, Characterization and Catalytic Properties in Alkylphenol Oxidation

Titanosilicates are a family of porous materials that have shown excellent catalytic activity in olefin epoxidation and many other selective oxidation reactions. These materials have been extensively studied in the past few decades. One of the central questions in these investigations has been to define the catalytically active centers. The traditional synthesis of wet impregnation followed by calcination often produces more than one type of catalytic center within the same matrix, i.e. different coordination, connectivity and nuclearity. Each type potentially possesses unique catalytic activity. Co-existence of multiple catalytic centers makes it impossible to establish the relationship between the structure of titanium center and catalytic activity. The goal of this research was to establish a structure-function relationship in titanosilicate materials through targeted synthesis of single-site nanostructured catalysts, in which titanium centers are isolated from one another while possessing the uniform structure. A building block synthetic methodology was utilized to prepare a series of catalysts, namely 2-connected (2C), 3-connected (3C), 4-connected (4C) and tetranuclear (Ti4 [subscript 4]) titanium catalysts. These materials were characterized in detail via gravimetric analysis, solid state NMR, diffuse reflectance UV spectroscopy, infrared spectroscopy, BET surface area and X-ray absorption spectroscopy (XANES and EXAFS). Catalytic activity of each catalyst was examined in the oxidation of 2, 3, 6-trimethylphenol with aqueous hydrogen peroxide to give the corresponding benzoquinone. Under identical conditions, high to mediocre catalytic activity has been observed in a sequence of Ti4 [subscript 4] \u3e 2C \u3e 3C ≈ 4C, in terms of both conversion and selectivity. All synthesized catalysts showed excellent stability and recyclability with aqueous hydrogen peroxide at elevated temperature. A structure-function relationship was therefore developed through targeted synthesis of novel single-site titanosilicate catalysts

More is Different: Modern Computational Modeling for Heterogeneous Catalysis

La combinació d'observacions experimentals i estudis de la Density Functional Theory (DFT) és un dels pilars de la investigació química moderna. Atès que permeten recopilar informació física addicional d'un sistema químic, difícilment accessible a través de l'entorn experimental, aquests estudis es fan servir àmpliament per modelar i predir el comportament d'una gran varietat de compostos químics en entorns únics. A la catàlisi heterogènia, els models DFT s'utilitzen habitualment per avaluar la interacció entre els compostos moleculars i els catalitzadors, vinculant aquestes interpretacions amb els resultats experimentals. Tanmateix, l'alta complexitat trobada tant als escenaris catalítics com a la reactivitat, implica la necessitat de metodologies sofisticades que requereixen automatització, emmagatzematge i anàlisi per estudiar correctament aquests sistemes. Aquest treball presenta el desenvolupament i la combinació de múltiples metodologies per avaluar correctament la complexitat d'aquests sistemes químics. A més, aquest treball mostra com s'han utilitzat les tècniques proporcionades per estudiar noves configuracions catalítiques d'interès acadèmic i industrial.La combinación de observaciones experimentales y estudios de la Density Functional Theory (DFT) es uno de los pilares de la investigación química moderna. Dado que permiten recopilar información física adicional de un sistema químico, difícilmente accesible a través del entorno experimental, estos estudios se emplean ampliamente para modelar y predecir el comportamiento de una gran variedad de compuestos químicos en entornos únicos. En la catálisis heterogénea, los modelos DFT se emplean habitualmente para evaluar la interacción entre los compuestos moleculares y los catalizadores, vinculando estas interpretaciones con los resultados experimentales. Sin embargo, la alta complejidad encontrada tanto en los escenarios catalíticos como en la reactividad, implica la necesidad de metodologías sofisticadas que requieren de automatización, almacenamiento y análisis para estudiar correctamente estos sistemas. Este trabajo presenta el desarrollo y la combinación de múltiples metodologías con el objetivo de evaluar correctamente la complejidad de estos sistemas químicos. Además, este trabajo muestra cómo las técnicas proporcionadas se han utilizado para estudiar nuevas configuraciones catalíticas de interés académico e industrial.The combination of Experimental observations and Density Functional Theory studies is one of the pillars of modern chemical research. As they enable the collection of additional physical information of a chemical system, hardly accessible via the experimental setting, Density Functional Theory studies are widely employed to model and predict the behavior of a diverse variety of chemical compounds under unique environments. Particularly, in heterogeneous catalysis, Density Functional Theory models are commonly employed to evaluate the interaction between molecular compounds and catalysts, lately linking these interpretations with experimental results. However, high complexity found in both, catalytic settings and reactivity, implies the need of sophisticated methodologies involving automation, storage and analysis to correctly study these systems. Here, I present the development and combination of multiple methodologies, aiming at correctly asses complexity. Also, this work shows how the provided techniques have been actively used to study novel catalytic settings of academic and industrial interest

Soft Computing

Soft computing is used where a complex problem is not adequately specified for the use of conventional math and computer techniques. Soft computing has numerous real-world applications in domestic, commercial and industrial situations. This book elaborates on the most recent applications in various fields of engineering

Green synthetic fuels: Renewable routes for the conversion of non-fossil feedstocks into gaseous fuels and their end uses

Innovative renewable routes are potentially able to sustain the transition to a decarbonized energy economy. Green synthetic fuels, including hydrogen and natural gas, are considered viable alternatives to fossil fuels. Indeed, they play a fundamental role in those sectors that are di cult to electrify (e.g., road mobility or high-heat industrial processes), are capable of mitigating problems related to flexibility and instantaneous balance of the electric grid, are suitable for large-size and long-term storage and can be transported through the gas network. This article is an overview of the overall supply chain, including production, transport, storage and end uses. Available fuel conversion technologies use renewable energy for the catalytic conversion of non-fossil feedstocks into hydrogen and syngas. We will show how relevant technologies involve thermochemical, electrochemical and photochemical processes. The syngas quality can be improved by catalytic CO and CO2 methanation reactions for the generation of synthetic natural gas. Finally, the produced gaseous fuels could follow several pathways for transport and lead to different final uses. Therefore, storage alternatives and gas interchangeability requirements for the safe injection of green fuels in the natural gas network and fuel cells are outlined. Nevertheless, the effects of gas quality on combustion emissions and safety are considered

Soft Computing

Soft computing is used where a complex problem is not adequately specified for the use of conventional math and computer techniques. Soft computing has numerous real-world applications in domestic, commercial and industrial situations. This book elaborates on the most recent applications in various fields of engineering

Arquitectura de búsqueda basada en técnicas soft computing para la resolución de problemas combinatorios en diferentes dominios de aplicación

En los problemas de optimización combinatoria se estudian colecciones finitas de objetos que satisfacen unos criterios específicos y se persigue determinar si cierto objeto ``óptimo'' existe. En la mayoría de las ocasiones, a pesar de que el dominio de búsqueda es finito, éste puede ser de dimensiones exponenciales. En la actualidad es posible solucionar un gran número de problemas combinatorios presentes en la vida real empleando técnicas basadas en programación entera. Sin embargo, en numerosas ocasiones no es posible resolverlos de forma exacta debido a la gran dificultad que presentan algunos problemas de optimización combinatoria y sólo es posible encontrar soluciones cercanas al óptimo. Para estas ocasiones, los esfuerzos de investigación se han centrado en la aplicación de técnicas meta-heurísticas. En este último caso se enmarca el presente trabajo, es decir, en la resolución de problemas combinatorios complejos, de grandes dimensiones, donde explorar todas las posibilidades a fin de encontrar el óptimo es inabordable, ya sea por motivos económicos (probar cada combinación sea caro) o por motivos computacionales (temporalmente sea intratable). En concreto, en esta tesis se propone una arquitectura de búsqueda independiente del dominio de aplicación y capaz de abordar problemas combinatorios de grandes dimensiones, de los que se disponga de poca información de partida. Esta arquitectura está basada en técnicas Soft Computing, pues combina un algoritmo genético basado en codificación real con modelos basados en redes neuronales, concretamente en perceptrones multicapa. Así, el algoritmo genético emplea, en los casos en los que sea necesario, modelos aproximados de las funciones de aptitud mediante perceptrones diseñados para tal fin. El sistema obtenido ofrece la flexibilidad y versatilidad requeridas para poder adaptarse a los requisitos propios de cada problema combinatorio a tratar, sea cual sea su dominio.Valero Cubas, S. (2010). Arquitectura de búsqueda basada en técnicas soft computing para la resolución de problemas combinatorios en diferentes dominios de aplicación [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8329Palanci