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

Computational characterization and prediction of metal-organic framework properties

In this introductory review, we give an overview of the computational chemistry methods commonly used in the field of metal-organic frameworks (MOFs), to describe or predict the structures themselves and characterize their various properties, either at the quantum chemical level or through classical molecular simulation. We discuss the methods for the prediction of crystal structures, geometrical properties and large-scale screening of hypothetical MOFs, as well as their thermal and mechanical properties. A separate section deals with the simulation of adsorption of fluids and fluid mixtures in MOFs

Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

Design of Single-Atom Catalysts and Tracking Their Fate Using Operando and Advanced X-ray Spectroscopic Tools

The potential of operando X-ray techniques for following the structure, fate, and active site of single-atom catalysts (SACs) is highlighted with emphasis on a synergetic approach of both topics. X-ray absorption spectroscopy (XAS) and related X-ray techniques have become fascinating tools to characterize solids and they can be applied to almost all the transition metals deriving information about the symmetry, oxidation state, local coordination, and many more structural and electronic properties. SACs, a newly coined concept, recently gained much attention in the field of heterogeneous catalysis. In this way, one can achieve a minimum use of the metal, theoretically highest efficiency, and the design of only one active site-so-called single site catalysts. While single sites are not easy to characterize especially under operating conditions, XAS as local probe together with complementary methods (infrared spectroscopy, electron microscopy) is ideal in this research area to prove the structure of these sites and the dynamic changes during reaction. In this review, starting from their fundamentals, various techniques related to conventional XAS and X-ray photon in/out techniques applied to single sites are discussed with detailed mechanistic and in situ/operando studies. We systematically summarize the design strategies of SACs and outline their exploration with XAS supported by density functional theory (DFT) calculations and recent machine learning tools

Confining isolated atoms and clusters in crystalline porous materials for catalysis

[EN] Structure-reactivity relationships for nanoparticle-based catalysts have been greatly influenced by the study of catalytic materials with either supported isolated metal atoms or metal clusters comprising a few atoms. The stability of these metal species is a key challenge because they can sinter into large nanoparticles under harsh reaction conditions. However, stability can be achieved by confining the nanoparticles in crystalline porous materials (such as zeolites and metal-organic frameworks). More importantly, the interaction between the metal species and the porous framework may modulate the geometric and electronic structures of the subnanometric metal species, especially for metal clusters. This confinement effect can induce shape-selective catalysis or different chemoselectivity from that of metal atoms supported on open-structure solid carriers. In this Review, we discuss the structural features, synthesis methodologies, characterization techniques and catalytic applications of subnanometric species confined in zeolites and metal-organic frameworks. We make a critical comparison between confined and non-confined isolated atoms and metal clusters, and provide future perspectives for the field.We are grateful for financial support from the European Research Council (grant ERC-AdG-2014-671093, SynCatMatch) and the Spanish Government through the Severo Ochoa Program (SEV-2016-0683).Liu, L.; Corma Canós, A. (2021). Confining isolated atoms and clusters in crystalline porous materials for catalysis. Nature Reviews Materials. 6(3):244-263. https://doi.org/10.1038/s41578-020-00250-32442636

hITeQ: A new workflow-based computing environment for streamlining discovery. Application in materials science

[EN] This paper presents the implementation of the recent methodology called Adaptable Time Warping (ATW) for the automatic identification of mixture of crystallographic phases from powder X-ray diffraction data, inside the framework of a new integrative platform named hITeQ. The methodology is encapsulated into a so-called workflow, and we explore the benefits of such an environment for streamlining discovery in R&D. Beside the fact that ATW successfully identifies and classifies crystalline phases from powder XRD for the very complicated case of zeolite ITQ-33 for which has been employed a high throughput synthesis process, we stress on the numerous difficulties encountered by academic laboratories and companies when facing the integration of new software or techniques. It is shown how an integrative approach provides a real asset in terms of cost, efficiency, and speed due to a unique environment that supports well-defined and reusable processes, improves knowledge management, and handles properly multi-disciplinary teamwork, and disparate data structures and protocols.EU Commission FP6 (TOPCOMBI Project) is gratefully acknowledged.Baumes, LA.; Jiménez Serrano, S.; Corma Canós, A. (2011). hITeQ: A new workflow-based computing environment for streamlining discovery. Application in materials science. Catalysis Today. 159(1):126-137. doi:10.1016/j.cattod.2010.03.067S126137159

Applications of Zeolites to C1 Chemistry: Recent Advances, Challenges, and Opportunities

[EN] C1 chemistry, which is the catalytic transformation of C1 molecules including CO, CO2, CH4, CH3OH, and HCOOH, plays an important role in providing energy and chemical supplies while meeting environmental requirements. Zeolites are highly efficient solid catalysts used in the chemical industry. The design and development of zeolite-based mono-, bi-, and multifunctional catalysts has led to a booming application of zeolite-based catalysts to C1 chemistry. Combining the advantages of zeolites and metallic catalytic species has promoted the catalytic production of various hydrocarbons (e.g., methane, light olefins, aromatics, and liquid fuels) and oxygenates (e.g., methanol, dimethyl ether, formic acid, and higher alcohols) from C1 molecules. The key zeolite descriptors that influence catalytic performance, such as framework topologies, nanoconfinement effects, Bronsted acidities, secondary-pore systems, particle sizes, extraframework cations and atoms, hydrophobicity and hydrophilicity, and proximity between acid and metallic sites are discussed to provide a deep understanding of the significance of zeolites to C1 chemistry. An outlook regarding challenges and opportunities for the conversion of C1 resources using zeolite-based catalysts to meet emerging energy and environmental demands is also presented.The authors thank the National Natural Science Foundation of China (Grants 21920102005, 21835002, and 21621001), the National Key Research and Development Program of China (Grant 2016YFB0701100), the 111 Project of China (B17020), and the Spanish Government through "Severo Ochoa" (SEV-2016-0683, MINECO) and PGC2018-101247-B-I00 for supporting this work.Zhang, Q.; Yu, J.; Corma Canós, A. (2020). 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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). At present, we have libraries of over ten thousand synthesized materials and millions of in-silico predicted materials. The fact that we have so many materials opens many exciting avenues to tailor make a material that is optimal for a given application. However, from an experimental and computational point of view we simply have too many materials to screen using brute-force techniques. 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 emphasize the importance of data collection, methods to augment small data sets, how to select appropriate training sets. An important part of this review are the different approaches that are used to represent these materials in feature space. The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised ML. In particular, we review the different method to optimize the ML process and how to quantify the performance of the different methods. In the second part, we review how the different approaches of ML 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. The range of topics illustrates the large variety of topics that can be studied with big-data science. Given the increasing interest of the scientific community in ML, we expect this list to rapidly expand in the coming years.Comment: Editorial changes (typos fixed, minor adjustments to figures