Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal-Organic Framework Space

Global warming is a crisis that humanity must face together. With greenhouse gases (GHGs) as the main factor causing global warming, the adoption of relevant processes to eliminate them is essential. With the advantages of high specific surface area, large pore volume, and tunable synthesis, metal-organic frameworks (MOFs) have attracted much attention in GHG storage, adsorption, separation, and catalysis. However, as the pool of MOFs expands rapidly with new syntheses and discoveries, finding a suitable MOF for a particular application is highly challenging. In this regard, high-throughput computational screening is considered the most effective research method for screening a large number of materials to discover high-performance target MOFs. Typically, high-throughput computational screening generates voluminous and multidimensional data, which is well suited for machine learning (ML) training to improve the screening efficiency and explore the relationships between the multidimensional data in depth. This Review summarizes the general process and common methods for using ML to screen MOFs in the field of GHG removal. It also addresses the challenges faced by ML in exploring the MOF space and potential directions for the future development of ML for MOF screening. This aims to enhance the understanding of the integration of ML and MOFs in various fields and broaden the application and development ideas of MOFs.

CO2 Hydrogenation to Gasoline and Aromatics: Mechanistic and Predictive Insights from DFT, DRIFTS and Machine Learning

The emission of CO2 from fossil fuels is the largest driver of global climate change. To realize the target of a carbon-neutrality by 2050, CO2 capture and utilization is crucial. The efficient conversion of CO2 to C5+ gasoline and aromatics remains elusive mainly due to CO2 thermodynamic stability and the high energy barrier of the C-C coupling step. Herein, advances in mechanistic understanding via Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS), density functional theory (DFT), and microkinetic modeling are discussed. It further emphasizes the power of machine learning (ML) to accelerate the search for optimal catalysts. A significant effort has been invested into this field of research with volumes of experimental and characterization data, this study discusses how they can be used as input features for machine learning prediction in a bid to better understand catalytic properties capable of accelerating breakthroughs in the process.

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.

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.

Photocatalytic CO2 Conversion Using Metal-Containing Coordination Polymers and Networks: Recent Developments in Material Design and Mechanistic Details

International guidelines have progressively addressed global warming which is caused by the greenhouse effect. The greenhouse effect originates from the atmosphere’s gases which trap sunlight which, as a consequence, causes an increase in global surface temperature. Carbon dioxide is one of these greenhouse gases and is mainly produced by anthropogenic emissions. The urgency of removing atmospheric carbon dioxide from the atmosphere to reduce the greenhouse effect has initiated the development of methods to covert carbon dioxide into valuable products. One approach that was developed is the photocatalytic transformation of CO2. Photocatalysis addresses environmental issues by transferring CO2 into value added chemicals by mimicking the natural photosynthesis process. During this process, the photocatalytic system is excited by light energy. CO2 is adsorbed at the catalytic metal centers where it is subsequently reduced. To overcome several obstacles for achieving an efficient photocatalytic reduction process, the use of metal-containing polymers as photocatalysts for carbon dioxide reduction is highlighted in this review. The attention of this manuscript is directed towards recent advances in material design and mechanistic details of the process using different polymeric materials and photocatalysts.

Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides

Catalytic-materials design requires predictive modeling of the interaction between catalyst and reactants. This is challenging due to the complexity and diversity of structure-property relationships across the chemical space. Here, we report a strategy for a rational design of catalytic materials using the artificial intelligence approach (AI) subgroup discovery. We identify catalyst genes (features) that correlate with mechanisms that trigger, facilitate, or hinder the activation of carbon dioxide (CO2) towards a chemical conversion. The AI model is trained on first-principles data for a broad family of oxides. We demonstrate that surfaces of experimentally identified good catalysts consistently exhibit combinations of genes resulting in a strong elongation of a C-O bond. The same combinations of genes also minimize the OCO-angle, the previously proposed indicator of activation, albeit under the constraint that the Sabatier principle is satisfied. Based on these findings, we propose a set of new promising catalyst materials for CO2 conversion.

Porous Organic Polymers for Catalytic Conversion of Carbon Dioxide

To overcome the challenges of global warming and environmental pollution, it is necessary to reduce the concentration of carbon dioxide (CO₂ ) in the atmosphere, which is mainly accumulated in the air through the burning of fossil fuels. Therefore, the development of environmentally friendly strategies to capture carbon dioxide and convert it into value-added products offers a promising way forward for reducing carbon dioxide concentration in the atmosphere. In this context, POPs (porous organic polymers) have shown great potential as CO₂ selective adsorbents due to their high specific surface area, chemical stability, nanoscale porosity and structural diversity, as well as POPs based heterogeneous catalysts for CO₂ conversion. This review provides a concise account of preparation methods of various POPs, challenges and current development trends of POPs in photocatalytic CO₂ reduction, electrocatalytic CO₂ reduction and chemical CO₂ conversion.

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.

Multi-factor study of the effects of a trace amount of water vapor on low concentration CO2 capture by 5A zeolite particles

CO₂ adsorption amount is enhanced with below 0.1 ppm humidity, and water molecule partial charge is a dominant factor in adsorption.

Emerging trends in porous materials for CO2 capture and conversion

This review highlights the recent progress in porous materials (MOFs, zeolites, POPs, nanoporous carbons, and mesoporous materials) for CO₂ capture and conversion.

A Robust Machine Learning Algorithm for the Prediction of Methane Adsorption in Nanoporous Materials

In the present study, we propose a new set of descriptors that, along with a few structural features of nanoporous materials, can be used by machine learning algorithms for accurate predictions of the gas uptake capacities of these materials. All new descriptors closely resemble the helium atom void fraction of the material framework. However, instead of a helium atom, a particle with an appropriately defined van der Waals radius is used. The set of void fractions of a small number of these particles is found to be sufficient to characterize uniquely the structure of each material and to account for the most important topological features. We assess the accuracy of our approach by examining the predictions of the random forest algorithm in the relative small dataset of the computation-ready, experimental (CoRE) MOFs (∼4700 structures) that have been experimentally synthesized and whose geometrical/structural features have been accurately calculated before. We first performed grand canonical Monte Carlo simulations to accurately determine their methane uptake capacities at two different temperatures (280 and 298 K) and three different pressures (1, 5.8, and 65 bar). Despite the high chemical and structural diversity of the CoRE MOFs, it was found that the use of the proposed descriptors significantly improves the accuracy of the machine learning algorithm, particularly at low pressures, compared to the predictions made based solely on the rest structural features. More importantly, the algorithm can be easily adapted for other types of nanoporous materials beyond MOFs. Convergence of the predictions was reached even for small training set sizes compared to what was found in previous works using the hypothetical MOF database.

pywindow: Automated Structural Analysis of Molecular Pores

Structural analysis of molecular pores can yield important information on their behavior in solution and in the solid state. We developed pywindow, a python package that enables the automated analysis of structural features of porous molecular materials, such as molecular cages. Our analysis includes the cavity diameter, number of windows, window diameters, and average molecular diameter. Molecular dynamics trajectories of molecular pores can also be analyzed to explore the influence of flexibility. We present the methodology, validation, and application of pywindow for the analysis of molecular pores, metal-organic polyhedra, and some instances of framework materials. pywindow is freely available from github.com/JelfsMaterialsGroup/pywindow .

Cutting Materials in Half: A Graph Theory Approach for Generating Crystal Surfaces and Its Prediction of 2D Zeolites

Scientific interest in two-dimensional (2D) materials, ranging from graphene and other single layer materials to atomically thin crystals, is quickly increasing for a large variety of technological applications. While in silico design approaches have made a large impact in the study of 3D crystals, algorithms designed to discover atomically thin 2D materials from their parent 3D materials are by comparison more sparse. We hypothesize that determining how to cut a 3D material in half (i.e., which Miller surface is formed) by severing a minimal number of bonds or a minimal amount of total bond energy per unit area can yield insight into preferred crystal faces. We answer this question by implementing a graph theory technique to mathematically formalize the enumeration of minimum cut surfaces of crystals. While the algorithm is generally applicable to different classes of materials, we focus on zeolitic materials due to their diverse structural topology and because 2D zeolites have promising catalytic and separation performance compared to their 3D counterparts. We report here a simple descriptor based only on structural information that predicts whether a zeolite is likely to be synthesizable in the 2D form and correctly identifies the expressed surface in known layered 2D zeolites. The discovery of this descriptor allows us to highlight other zeolites that may also be synthesized in the 2D form that have not been experimentally realized yet. Finally, our method is general since the mathematical formalism can be applied to find the minimum cut surfaces of other crystallographic materials such as metal-organic frameworks, covalent-organic frameworks, zeolitic-imidazolate frameworks, metal oxides, etc.

Materials Genome in Action: Identifying the Performance Limits of Physical Hydrogen Storage

The Materials Genome is in action: the molecular codes for millions of materials have been sequenced, predictive models have been developed, and now the challenge of hydrogen storage is targeted. Renewably generated hydrogen is an attractive transportation fuel with zero carbon emissions, but its storage remains a significant challenge. Nanoporous adsorbents have shown promising physical adsorption of hydrogen approaching targeted capacities, but the scope of studies has remained limited. Here the Nanoporous Materials Genome, containing over 850 000 materials, is analyzed with a variety of computational tools to explore the limits of hydrogen storage. Optimal features that maximize net capacity at room temperature include pore sizes of around 6 Å and void fractions of 0.1, while at cryogenic temperatures pore sizes of 10 Å and void fractions of 0.5 are optimal. Our top candidates are found to be commercially attractive as "cryo-adsorbents", with promising storage capacities at 77 K and 100 bar with 30% enhancement to 40 g/L, a promising alternative to liquefaction at 20 K and compression at 700 bar.

Materials space of solid-state electrolytes: unraveling chemical composition–structure–ionic conductivity relationships in garnet-type metal oxides using cheminformatics virtual screening approaches

Several candidate garnet-related compounds have been recommended for synthesis as potential materials for solid-state electrolytes.

A DFT Study of CO2 Hydrogenation on Faujasite-Supported Ir4 Clusters: on the Role of Water for Selectivity Control

Reaction mechanisms for the catalytic hydrogenation of CO₂ by faujasite‐supported Ir₄ clusters were studied by periodic DFT calculations. The reaction can proceed through two alternative paths. The thermodynamically favoured path results in the reduction of CO₂ to CO, whereas the other, kinetically preferred channel involves CO₂ hydrogenation to formic acid under water‐free conditions. Both paths are promoted by catalytic amounts of water confined inside the zeolite micropores with a stronger promotion effect for the reduction path. Co‐adsorbed water facilitates the cooperation between the zeolite Brønsted acid sites and Ir₄ cluster by opening low‐energy reaction channels for CO₂ conversion.

Competitive adsorption of VOCs from binary aqueous mixtures on zeolite ZSM-5

Adsorption equilibria of methyl tert-butyl ether (MTBE)/toluene (TOL), and 1,2-dichloroethane (DCE)/MTBE binary mixtures in aqueous solution on ZSM-5 were measured over a wide range of concentrations.