Gas adsorption and framework flexibility of CALF-20 explored via experiments and simulations.

In 2021, Svante, in collaboration with BASF, reported successful scale up of CALF-20 production, a stable MOF with high capacity for post-combustion CO2 capture which exhibits remarkable stability towards water. CALF-20's success story in the MOF commercialisation space provides new thinking about appropriate structural and adsorptive metrics important for CO2 capture. Here, we combine atomistic-level simulations with experiments to study adsorptive properties of CALF-20 and shed light on its flexible crystal structure. We compare measured and predicted CO2 and water adsorption isotherms and explain the role of water-framework interactions and hydrogen bonding networks in CALF-20's hydrophobic behaviour. Furthermore, regular and enhanced sampling molecular dynamics simulations are performed with both density-functional theory (DFT) and machine learning potentials (MLPs) trained to DFT energies and forces. From these simulations, the effects of adsorption-induced flexibility in CALF-20 are uncovered. We envisage this work would encourage development of other MOF materials useful for CO2 capture applications in humid conditions.

Computational Protocol for the Spectral Assignment of NMR Resonances in Covalent Organic Frameworks.

Solid-state nuclear magnetic resonance spectroscopy is routinely used in the field of covalent organic frameworks to elucidate or confirm the structure of the synthesized samples and to understand dynamic phenomena. Typically this involves the interpretation and simulation of the spectra through the assumption of symmetry elements of the building units, hinging on the correct assignment of each line shape. To avoid misinterpretation resulting from library-based assignment without a theoretical basis incorporating the impact of the framework, this work proposes a first-principles computational protocol for the assignment of experimental spectra, which exploits the symmetry of the underlying building blocks for computational feasibility. In this way, this protocol accommodates the validation of previous experimental assignments and can serve to complement new NMR measurements.

The Application of Porous Organic Polymers as Metal Free Photocatalysts in Organic Synthesis.

Concerns about increasing greenhouse gas emissions and their effect on our environment highlight the urgent need for new sustainable technologies. Visible light photocatalysis allows the clean and selective generation of reactive intermediates under mild conditions. The more widespread adoption of the current generation of photocatalysts, particularly those using precious metals, is hampered by drawbacks such as their cost, toxicity, difficult separation, and limited recyclability. This is driving the search for alternatives, such as porous organic polymers (POPs). This new class of materials is made entirely from organic building blocks, can possess high surface area and stability, and has a controllable composition and functionality. This review focuses on the application of POPs as photocatalysts in organic synthesis. For each reaction type, a representative material is discussed, with special attention to the mechanism of the reaction. Additionally, an overview is given, comparing POPs with other classes of photocatalysts, and critical conclusions and future perspectives are provided on this important field.

Transitioning from Methanol to Olefins (MTO) toward a Tandem CO2 Hydrogenation Process: On the Role and Fate of Heteroatoms (Mg, Si) in MAPO-18 Zeotypes.

The tandem CO2 hydrogenation to hydrocarbons over mixed metal oxide/zeolite catalysts (OXZEO) is an efficient way of producing value-added hydrocarbons (platform chemicals and fuels) directly from CO2via methanol intermediate in a single reactor. In this contribution, two MAPO-18 zeotypes (M = Mg, Si) were tested and their performance was compared under methanol-to-olefins (MTO) conditions (350 °C, PCH3OH = 0.04 bar, 6.5 gCH3OH h-1 g-1), methanol/CO/H2 cofeed conditions (350 °C, PCH3OH/PCO/PH2 = 1:7.3:21.7 bar, 2.5 gCH3OH h-1 g-1), and tandem CO2 hydrogenation-to-olefin conditions (350 °C, PCO2/PH2 = 7.5:22.5 bar, 1.4-12.0 gMAPO-18 h molCO2-1). In the latter case, the zeotypes were mixed with a fixed amount of ZnO:ZrO2 catalyst, well-known for the conversion of CO2/H2 to methanol. Focus was set on the methanol conversion activity, product selectivity, and performance stability with time-on-stream. In situ and ex situ Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), solid-state nuclear magnetic resonance (NMR), sorption experiments, and ab initio molecular dynamics (AIMD) calculations were performed to correlate material performance with material characteristics. The catalytic tests demonstrated the better performance of MgAPO-18 versus SAPO-18 at MTO conditions, the much superior performance of MgAPO-18 under methanol/CO/H2 cofeeds, and yet the increasingly similar performance of the two materials under tandem conditions upon increasing the zeotype-to-oxide ratio in the tandem catalyst bed. In situ FT-IR measurements coupled with AIMD calculations revealed differences in the MTO initiation mechanism between the two materials. SAPO-18 promoted initial CO2 formation, indicative of a formaldehyde-based decarboxylation mechanism, while CO and ketene were the main constituents of the initiation pool in MgAPO-18, suggesting a decarbonylation mechanism. Under tandem CO2 hydrogenation conditions, the presence of high water concentrations and low methanol partial pressure in the reaction medium led to lower, and increasingly similar, methanol turnover frequencies for the zeotypes. Despite both MAPO-18 zeotypes showing signs of activity loss upon storage due to the interaction of the sites with ambient humidity, they presented a remarkable stability after reaching steady state under tandem reaction conditions and after steaming and regeneration cycles at high temperatures. Water adsorption experiments at room temperature confirmed this observation. The faster activity loss observed in the Mg version is assigned to its harder Mg2+-ion character and the higher concentration of CHA defects in the AEI structure, identified by solid-state NMR and XRD. The low stability of a MgAPO-34 zeotype (CHA structure) upon storage corroborated the relationship between CHA defects and instability.

A Critical Assessment on Calculating Vibrational Spectra in Nanostructured Materials.

Vibrational spectroscopy is an omnipresent spectroscopic technique to characterize functional nanostructured materials such as zeolites, metal-organic frameworks (MOFs), and metal-halide perovskites (MHPs). The resulting experimental spectra are usually complex, with both low-frequency framework modes and high-frequency functional group vibrations. Therefore, theoretically calculated spectra are often an essential element to elucidate the vibrational fingerprint. In principle, there are two possible approaches to calculate vibrational spectra: (i) a static approach that approximates the potential energy surface (PES) as a set of independent harmonic oscillators and (ii) a dynamic approach that explicitly samples the PES around equilibrium by integrating Newton's equations of motions. The dynamic approach considers anharmonic and temperature effects and provides a more genuine representation of materials at true operating conditions; however, such simulations come at a substantially increased computational cost. This is certainly true when forces and energy evaluations are performed at the quantum mechanical level. Molecular dynamics (MD) techniques have become more established within the field of computational chemistry. Yet, for the prediction of infrared (IR) and Raman spectra of nanostructured materials, their usage has been less explored and remain restricted to some isolated successes. Therefore, it is currently not a priori clear which methodology should be used to accurately predict vibrational spectra for a given system. A comprehensive comparative study between various theoretical methods and experimental spectra for a broad set of nanostructured materials is so far lacking. To fill this gap, we herein present a concise overview on which methodology is suited to accurately predict vibrational spectra for a broad range of nanostructured materials and formulate a series of theoretical guidelines to this purpose. To this end, four different case studies are considered, each treating a particular material aspect, namely breathing in flexible MOFs, characterization of defects in the rigid MOF UiO-66, anharmonic vibrations in the metal-halide perovskite CsPbBr3, and guest adsorption on the pores of the zeolite H-SSZ-13. For all four materials, in their guest- and defect-free state and at sufficiently low temperatures, both the static and dynamic approach yield qualitatively similar spectra in agreement with experimental results. When the temperature is increased, the harmonic approximation starts to fail for CsPbBr3 due to the presence of anharmonic phonon modes. Also, the spectroscopic fingerprints of defects and guest species are insufficiently well predicted by a simple harmonic model. Both phenomena flatten the potential energy surface (PES), which facilitates the transitions between metastable states, necessitating dynamic sampling. On the basis of the four case studies treated in this Review, we can propose the following theoretical guidelines to simulate accurate vibrational spectra of functional solid-state materials: (i) For nanostructured crystalline framework materials at low temperature, insights into the lattice dynamics can be obtained using a static approach relying on a few points on the PES and an independent set of harmonic oscillators. (ii) When the material is evaluated at higher temperatures or when additional complexity enters the system, e.g., strong anharmonicity, defects, or guest species, the harmonic regime breaks down and dynamic sampling is required for a correct prediction of the phonon spectrum. These guidelines and their illustrations for prototype material classes can help experimental and theoretical researchers to enhance the knowledge obtained from a lattice dynamics study.

OGRe: Optimal Grid Refinement Protocol for Accurate Free Energy Surfaces and Its Application in Proton Hopping in Zeolites and 2D COF Stacking.

While free energy surfaces are the crux of our understanding of many chemical and biological processes, their accuracy is generally unknown. Moreover, many developments to improve their accuracy are often complicated, limiting their general use. Luckily, several tools and guidelines are already in place to identify these shortcomings, but they are typically lacking in flexibility or fail to systematically determine how to improve the accuracy of the free energy calculation. To overcome these limitations, this work introduces OGRe, a Python package for optimal grid refinement in an arbitrary number of dimensions. OGRe is based on three metrics that gauge the confinement, consistency, and overlap of each simulation in a series of umbrella sampling (US) simulations, an enhanced sampling technique ubiquitously adopted to construct free energy surfaces for hindered processes. As these three metrics are fundamentally linked to the accuracy of the weighted histogram analysis method adopted to generate free energy surfaces from US simulations, they facilitate the systematic construction of accurate free energy profiles, where each metric is driven by a specific umbrella parameter. This allows for the derivation of a consistent and optimal collection of umbrellas for each simulation, largely independent of the initial values, thereby dramatically increasing the ease-of-use toward accurate free energy surfaces. As such, OGRe is particularly suited to determine complex free energy surfaces with large activation barriers and shallow minima, which underpin many physical and chemical transformations and hence to further our fundamental understanding of these processes.

Super-Oxidizing Covalent Triazine Framework Electrocatalyst for Two-Electron Water Oxidation to H2 O2.

Electrochemical two-electron water oxidation (2e WOR) is gaining surging research traction for sustainable hydrogen peroxide production. However, the strong oxidizing environment and thermodynamically competitive side-reaction (4e WOR) posit as thresholds for the 2e WOR. We herein report a custom-crafted covalent triazine network possessing strong oxidizing properties as a proof-of-concept metal-free functional organic network electrocatalyst for catalyzing 2e WOR. As the first-of-its-kind, the material shows a maximum of 89.9 % Faradaic Efficiency and 1428 µmol/h/cm2 H2 O2 production rate at 3.0 V bias potential (vs reversible hydrogen electrode, RHE), which are either better or comparable to the state-of-the-art electrocatalysts. We have experimentally confirmed a stepwise 2e WOR mechanism which was further computationally endorsed by density functional theory studies.

The electrophilic aromatic bromination of benzenes: mechanistic and regioselective insights from density functional theory.

The HBr-assisted electrophilic aromatic bromination of benzene, anisole and nitrobenzene was investigated using static DFT calculations in gas phase and implicit apolar (CCl4) and polar (acetonitrile) solvent models at the ωB97X-D/cc-pVTZ level of theory. The reaction profiles corresponding to either a direct substitution reaction or an addition-elimination process were constructed and insight into the preferred regioselectivity was provided using a combination of conceptual DFT reactivity indices, aromaticity indices, Wiberg bond indices and the non-covalent interaction index. Our results show that under the considered reaction conditions the bromination reaction preferentially occurs through an addition-elimination mechanism and without formation of a stable charged Wheland intermediate. The ortho/para directing effect of the electron-donating methoxy-group in anisole was ascribed to a synergy between strong electron delocalisation and attractive interactions. In contrast, the preferred meta-addition on nitrobenzene could not be traced back to any of these effects, nor to the intrinsic reactivity property of the reactant. In this case, an electrostatic clash between the ipso-carbon of the ring and the nitrogen atom resulting from the later nature of the rate-determining step, with respect to anisole, appeared to play a crucial role.

Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations.

Within this Perspective, we critically reflect on the role of first-principles molecular dynamics (MD) simulations in unraveling the catalytic function within zeolites under operating conditions. First-principles MD simulations refer to methods where the dynamics of the nuclei is followed in time by integrating the Newtonian equations of motion on a potential energy surface that is determined by solving the quantum-mechanical many-body problem for the electrons. Catalytic solids used in industrial applications show an intriguing high degree of complexity, with phenomena taking place at a broad range of length and time scales. Additionally, the state and function of a catalyst critically depend on the operating conditions, such as temperature, moisture, presence of water, etc. Herein we show by means of a series of exemplary cases how first-principles MD simulations are instrumental to unravel the catalyst complexity at the molecular scale. Examples show how the nature of reactive species at higher catalytic temperatures may drastically change compared to species at lower temperatures and how the nature of active sites may dynamically change upon exposure to water. To simulate rare events, first-principles MD simulations need to be used in combination with enhanced sampling techniques to efficiently sample low-probability regions of phase space. Using these techniques, it is shown how competitive pathways at operating conditions can be discovered and how broad transition state regions can be explored. Interestingly, such simulations can also be used to study hindered diffusion under operating conditions. The cases shown clearly illustrate how first-principles MD simulations reveal insights into the catalytic function at operating conditions, which could not be discovered using static or local approaches where only a few points are considered on the potential energy surface (PES). Despite these advantages, some major hurdles still exist to fully integrate first-principles MD methods in a standard computational catalytic workflow or to use the output of MD simulations as input for multiple length/time scale methods that aim to bridge to the reactor scale. First of all, methods are needed that allow us to evaluate the interatomic forces with quantum-mechanical accuracy, albeit at a much lower computational cost compared to currently used density functional theory (DFT) methods. The use of DFT limits the currently attainable length/time scales to hundreds of picoseconds and a few nanometers, which are much smaller than realistic catalyst particle dimensions and time scales encountered in the catalysis process. One solution could be to construct machine learning potentials (MLPs), where a numerical potential is derived from underlying quantum-mechanical data, which could be used in subsequent MD simulations. As such, much longer length and time scales could be reached; however, quite some research is still necessary to construct MLPs for the complex systems encountered in industrially used catalysts. Second, most currently used enhanced sampling techniques in catalysis make use of collective variables (CVs), which are mostly determined based on chemical intuition. To explore complex reactive networks with MD simulations, methods are needed that allow the automatic discovery of CVs or methods that do not rely on a priori definition of CVs. Recently, various data-driven methods have been proposed, which could be explored for complex catalytic systems. Lastly, first-principles MD methods are currently mostly used to investigate local reactive events. We hope that with the rise of data-driven methods and more efficient methods to describe the PES, first-principles MD methods will in the future also be able to describe longer length/time scale processes in catalysis. This might lead to a consistent dynamic description of all steps-diffusion, adsorption, and reaction-as they take place at the catalyst particle level.

On the Prediction of Spectroscopic Fingerprints of Co2+ Complexes Relevant for the ZIF Nucleation Process.

The nucleation process of zeolitic imidazolate frameworks (ZIFs) is to date not completely understood. Recently, it has been found that, during the formation of Co-ZIF-67, after mixing imidazole-type ligands with octahedral precursors containing oxygen-coordinated ligands, a metal-organic pool with a diversity of transition metal complexes (TMCs) is formed showing fingerprints of octahedral and tetrahedral Co2+ complexes with both types of ligands [Filez, M. Cell Rep. Phys. Sci. 2021, 2, 100680]. In order to further unravel this process, we aim to characterize the d-d transitions of the TMCs and focus on their number, intensity, and position, which change during the process and can thus serve as a fingerprint for the formed species. It was previously shown that the number of ligands and symmetry has a detrimental influence on the ground state properties of Co2+ TMCs. Herein, we investigate how far excited state properties of TMCs relevant during nanoporous formation processes can be predicted by time-dependent density functional theory (TDDFT) and ligand field density functional theory (LFDFT). As TMCs are known to be challenging systems with possibly degenerate ground states and double excitations, we first investigate the performance of both techniques on first-row octahedral aqua-complexes. With this knowledge, we then focus on tetrahedral Co2+ complexes with aqua and imidazole-type ligands in order to investigate in how far we can propose a spectroscopic fingerprint that allows us to follow the Co2+ complexes during the formation of Co-ZIF-67. The results of TDDFT and LFDFT are qualitatively in agreement and provide complementary information. We found that various features can be used to distinguish between the species. However, as LFDFT is not suited for TMCs possessing the extended imidazole-type ligands and double and spin-flip states are not included in TDDFT, both techniques need to be complemented with more advanced methods to obtain complete insight into the d-d excitations of TMCs with imidazole ligands. Therefore, we particularly explored ab initio ligand field theory, which is capable of describing double excitations and is, in contrast to LFDFT, suitable for TMCs with extended ligands.

DFT-Quality Adsorption Simulations in Metal-Organic Frameworks Enabled by Machine Learning Potentials.

Nanoporous materials such as metal-organic frameworks (MOFs) have been extensively studied for their potential for adsorption and separation applications. In this respect, grand canonical Monte Carlo (GCMC) simulations have become a well-established tool for computational screenings of the adsorption properties of large sets of MOFs. However, their reliance on empirical force field potentials has limited the accuracy with which this tool can be applied to MOFs with challenging chemical environments such as open-metal sites. On the other hand, density-functional theory (DFT) is too computationally demanding to be routinely employed in GCMC simulations due to the excessive number of required function evaluations. Therefore, we propose in this paper a protocol for training machine learning potentials (MLPs) on a limited set of DFT intermolecular interaction energies (and forces) of CO2 in ZIF-8 and the open-metal site containing Mg-MOF-74, and use the MLPs to derive adsorption isotherms from first principles. We make use of the equivariant NequIP model which has demonstrated excellent data efficiency, and as such an error on the interaction energies below 0.2 kJ mol-1 per adsorbate in ZIF-8 was attained. Its use in GCMC simulations results in highly accurate adsorption isotherms and heats of adsorption. For Mg-MOF-74, a large dependence of the obtained results on the used dispersion correction was observed, where PBE-MBD performs the best. Lastly, to test the transferability of the MLP trained on ZIF-8, it was applied to ZIF-3, ZIF-4, and ZIF-6, which resulted in large deviations in the predicted adsorption isotherms and heats of adsorption. Only when explicitly training on data for all ZIFs, accurate adsorption properties were obtained. As the proposed methodology is widely applicable to guest adsorption in nanoporous materials, it opens up the possibility for training general-purpose MLPs to perform highly accurate investigations of guest adsorption.

Exploring the Charge Storage Dynamics in Donor-Acceptor Covalent Organic Frameworks Based Supercapacitors by Employing Ionic Liquid Electrolyte.

Two donor-acceptor type tetrathiafulvalene (TTF)-based covalent organic frameworks (COFs) are investigated as electrodes for symmetric supercapacitors in different electrolytes, to understand the charge storage and dynamics in 2D COFs. Till-date, most COFs are investigated as Faradic redox pseudocapacitors in aqueous electrolytes. For the first time, it is tried to enhance the electrochemical performance and stability of pristine COF-based supercapacitors by operating them in the non-Faradaic electrochemically double layer capacitance region. It is found that the charge storage mechanism of ionic liquid (IL) electrolyte based supercapacitors is dependent on the micropore size and surface charge density of the donor-acceptor COFs. The surface charge density alters due to the different electron acceptor building blocks, which in turn influences the dense packing of the IL near its pore. The micropores induce pore confinement of IL in the COFs by partial breaking of coulomb ordering and rearranging it. The combination of these two factors enhance the charge storage in the highly microporous COFs. The density functional theory calculations support the same. At 1 A g-1 , TTF-porphyrin COF provides capacitance of 42, 70, and 130 F g-1 in aqueous, organic, and IL electrolyte respectively. TTF-diamine COF shows a similar trend with 100 F g-1 capacitance in IL.

Engineering of Phenylpyridine- and Bipyridine-Based Covalent Organic Frameworks for Photocatalytic Tandem Aerobic Oxidation/Povarov Cyclization.

Covalent organic frameworks (COFs) are emerging as a new class of photoactive organic semiconductors, which possess crystalline ordered structures and high surface areas. COFs can be tailor-made toward specific (photocatalytic) applications, and the size and position of their band gaps can be tuned by the choice of building blocks and linkages. However, many types of building blocks are still unexplored as photocatalytic moieties and the scope of reactions photocatalyzed by COFs remains quite limited. In this work, we report the synthesis and application of two bipyridine- or phenylpyridine-based COFs: TpBpyCOF and TpPpyCOF. Due to their good photocatalytic properties, both materials were applied as metal-free photocatalysts for the tandem aerobic oxidation/Povarov cyclization and α-oxidation of N-aryl glycine derivatives, with the bipyridine-based TpBpyCOF exhibiting the highest activity. By expanding the range of reactions that can be photocatalyzed by COFs, this work paves the way toward the more widespread application of COFs as metal-free heterogeneous photocatalysts as a convenient alternative for commonly used homogeneous (metal-based) photocatalysts.

Simulations in the era of exascale computing.

Exascale computers - supercomputers that can perform 1018 floating point operations per second - started coming online in 2022: in the United States, Frontier launched as the first public exascale supercomputer and Aurora is due to open soon; OceanLight and Tianhe-3 are operational in China; and JUPITER is due to launch in 2023 in Europe. Supercomputers offer unprecedented opportunities for modelling complex materials. In this Viewpoint, five researchers working on different types of materials discuss the most promising directions in computational materials science.

Challenges in modelling dynamic processes in realistic nanostructured materials at operating conditions.

The question is addressed in how far current modelling strategies are capable of modelling dynamic phenomena in realistic nanostructured materials at operating conditions. Nanostructured materials used in applications are far from perfect; they possess a broad range of heterogeneities in space and time extending over several orders of magnitude. Spatial heterogeneities from the subnanometre to the micrometre scale in crystal particles with a finite size and specific morphology, impact the material's dynamics. Furthermore, the material's functional behaviour is largely determined by the operating conditions. Currently, there exists a huge length-time scale gap between attainable theoretical length-time scales and experimentally relevant scales. Within this perspective, three key challenges are highlighted within the molecular modelling chain to bridge this length-time scale gap. Methods are needed that enable (i) building structural models for realistic crystal particles having mesoscale dimensions with isolated defects, correlated nanoregions, mesoporosity, internal and external surfaces; (ii) the evaluation of interatomic forces with quantum mechanical accuracy albeit at much lower computational cost than the currently used density functional theory methods and (iii) derivation of the kinetics of phenomena taking place in a multi-length-time scale window to obtain an overall view of the dynamics of the process. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'.

Pyrene-Based Covalent Organic Frameworks for Photocatalytic Hydrogen Peroxide Production.

Four highly porous covalent organic frameworks (COFs) containing pyrene units were prepared and explored for photocatalytic H2 O2 production. The experimental studies are complemented by density functional theory calculations, proving that the pyrene unit is more active for H2 O2 production than the bipyridine and (diarylamino)benzene units reported previously. H2 O2 decomposition experiments verified that the distribution of pyrene units over a large surface area of COFs plays an important role in catalytic performance. The Py-Py-COF though contains more pyrene units than other COFs which induces a high H2 O2 decomposition due to a dense concentration of pyrene in close proximity over a limited surface area. Therefore, a two-phase reaction system (water-benzyl alcohol) was employed to inhibit H2 O2 decomposition. This is the first report on applying pyrene-based COFs in a two-phase system for photocatalytic H2 O2 generation.

Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics.

Proton hopping is a key reactive process within zeolite catalysis. However, the accurate determination of its kinetics poses major challenges both for theoreticians and experimentalists. Nuclear quantum effects (NQEs) are known to influence the structure and dynamics of protons, but their rigorous inclusion through the path integral molecular dynamics (PIMD) formalism was so far beyond reach for zeolite catalyzed processes due to the excessive computational cost of evaluating all forces and energies at the Density Functional Theory (DFT) level. Herein, we overcome this limitation by training first a reactive machine learning potential (MLP) that can reproduce with high fidelity the DFT potential energy surface of proton hopping around the first Al coordination sphere in the H-CHA zeolite. The MLP offers an immense computational speedup, enabling us to derive accurate reaction kinetics beyond standard transition state theory for the proton hopping reaction. Overall, more than 0.6 µs of simulation time was needed, which is far beyond reach of any standard DFT approach. NQEs are found to significantly impact the proton hopping kinetics up to ~473 K. Moreover, PIMD simulations with deuterium can be performed without any additional training to compute kinetic isotope effects over a broad range of temperatures.