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Through a data-mining and high-throughput density functional theory approach, we identify a diverse range of metallic compounds that are predicted to have transition metals with "free-atom-like" d states that are highly localized in terms of their energetic distribution. Design principles that favor the formation of localized d states are uncovered, among which we note that site isolation is often necessary but that the dilute limit, as in most single-atom alloys, is not a pre-requisite. Additionally, the majority of localized d state transition metals identified from the computational screening study exhibit partial anionic character due to charge transfer from neighboring metal species. Using CO as a representative probe molecule, we show that localized d states for Rh, Ir, Pd, and Pt tend to reduce the binding strength of CO compared to their pure elemental analogues, whereas this does not occur as consistently for the Cu binding sites. These trends are rationalized through the d-band model, which suggests that the significantly reduced d-band width results in an increased orthogonalization energy penalty upon CO chemisorption. With the multitude of inorganic solids that are predicted to have highly localized d states, the results of the screening study are likely to result in new avenues for heterogeneous catalyst design from an electronic structure perspective.

An iron–triazolate metal–organic framework (MOF) is computationally investigated for the catalytic oxidation of strong C–H bonds. The MOF is predicted to form reactive iron-oxo active sites, and design rules to guide future experiments are discussed.

The space of all plausible materials for a given application is so large that it cannot be explored using a brute-force approach. This is, in particular, the case for reticular chemistry which provides materials designers with a practically infinite playground on different length scales. One promising approach to guide the design and discovery of materials is machine learning, which typically involves learning a mapping of structures onto properties from data. While there have been plenty of examples of the use of machine learning for reticular materials, the progress in the field seems to have stagnated. From our perspective, an important reason is that digital reticular chemistry is still more an art than a science in which many parts are only accessible to experienced groups. The lack of standardization across all the steps of the machine learning pipeline makes it practically impossible to directly compare machine learning models and build on top of prior results. To confront these challenges, we present mofdscribe: a software ecosystem that accompanies—seasoned as well as novice—digital reticular chemists on all steps from ideation to model publication. Our package provides reference datasets (including a completely new one), more than 35 reported as well as completely novel featurization strategies, data splitters, and validation helpers which can be used to benchmark new modeling strategies on standard benchmark tasks and to report the results on a public leaderboard. We envision that this ecosystem allows for a more robust, comparable, and productive area of digital reticular chemistry.

Summary The modular nature of metal–organic frameworks (MOFs) enables synthetic control over their physical and chemical properties, but it can be difficult to know which MOFs would be optimal for a given application. High-throughput computational screening and machine learning are promising routes to efficiently navigate the vast chemical space of MOFs but have rarely been used for the prediction of properties that need to be calculated by quantum mechanical methods. Here, we introduce the Quantum MOF (QMOF) database, a publicly available database of computed quantum-chemical properties for more than 14,000 experimentally synthesized MOFs. Throughout this study, we demonstrate how machine learning models trained on the QMOF database can be used to rapidly discover MOFs with targeted electronic structure properties, using the prediction of theoretically computed band gaps as a representative example. We conclude by highlighting several MOFs predicted to have low band gaps, a challenging task given the electronically insulating nature of most MOFs.

Zirconium-based metal–organic frameworks (Zr-MOFs) have been increasingly studied over the past two decades as heterogeneous catalysts due to their synthetic tunability, well-defined nature, and ch...

Through quantum-chemical calculations, we investigate a family of metal-organic frameworks (MOFs) containing triazolate linkers, M2 X2 (BBTA) (M=metal, X=bridging anion, H2 BBTA=1H,5H-benzo(1,2-d:4,5-d')bistriazole), for their ability to form terminal metal-oxo sites and subsequently activate the C-H bond of methane. By varying the metal and bridging anion in the framework, we show how to significantly tune the reactivity of this series of MOFs. The electronic structure of the metal-oxo active site is analyzed for each combination of metal and bridging ligand, and we find that spin density localized on the oxo ligand is not an inherent requirement for low C-H activation barriers. For the Mn- and Fe-containing frameworks, a transition from ferromagnetic to antiferromagnetic coupling between the metal binding site and terminal oxo ligand during the C-H activation process can greatly reduce the kinetic barrier, a unique case of two-state reactivity without a change in the net spin multiplicity.

Metal-organic frameworks (MOFs) with coordinatively unsaturated metal sites are appealing as adsorbent materials due to their tunable functionality and ability to selectively bind small molecules. Through the use of computational screening methods based on periodic density functional theory, we investigate O2 and N2 adsorption at the coordinatively unsaturated metal sites of several MOF families. A variety of design handles are identified that can be used to modify the redox activity of the metal centers, including changing the functionalization of the linkers (replacing oxido donors with sulfido donors), anion exchange of bridging ligands (considering μ-Br-, μ-Cl-, μ-F-, μ-SH-, or μ-OH- groups), and altering the formal oxidation state of the metal. As a result, we show that it is possible to tune the O2 affinity at the open metal sites of MOFs for applications involving the strong and/or selective binding of O2. In contrast with O2 adsorption, N2 adsorption at open metal sites is predicted to be relatively weak across the MOF dataset, with the exception of MOFs containing synthetically elusive V2+ open metal sites. As one example from the screening study, we predicted that exchanging the μ-Cl- ligands of M2Cl2(BBTA) (H2BBTA = 1H,5H-benzo(1,2-d:4,5-d')bistriazole) with μ-OH- groups would significantly enhance the strength of O2 adsorption at the open metal sites without a corresponding increase in the N2 affinity. Experimental investigation of Co2Cl2(BBTA) and Co2(OH)2(BBTA) confirms that the former exhibits weak physisorption of both N2 and O2, whereas the latter is capable of chemisorbing O2 at room temperature in a highly selective manner. The O2 chemisorption behavior is attributed to the greater electron-donating character of the μ-OH- ligands and the presence of H-bonding interactions between the μ-OH- bridging ligands and the reduced O2 adsorbate.

The development of adsorbents with molecular precision offers a promising strategy to enhance storage of hydrogen and methane─considered the fuel of the future and a transitional fuel, respectively─and to realize a carbon-neutral energy cycle. Herein we employ a postsynthetic modification strategy on a robust metal-organic framework (MOF), MFU-4l, to boost its storage capacity toward these clean energy gases. MFU-4l-Li displays one of the best volumetric deliverable hydrogen capacities of 50.2 g L-1 under combined temperature and pressure swing conditions (77 K/100 bar → 160 K/5 bar) while maintaining a moderately high gravimetric capacity of 9.4 wt %. Moreover, MFU-4l-Li demonstrates impressive methane storage performance with a 5-100 bar usable capacity of 251 cm3 (STP) cm-3 (0.38 g g-1) and 220 cm3 (STP) cm-3 (0.30 g g-1) at 270 and 296 K, respectively. Notably, these hydrogen and methane storage capacities are significantly improved compared to those of its isoreticular analogue, MFU-4l, and place MFU-4l-Li among the best MOF-based materials for this application.

The modular nature of metal–organic frameworks (MOFs) leads to a very large number of possible structures. High-throughput computational screening has led to a rapid increase in property data that ...

In this work, we leverage advances in computational screening based on periodic density functional theory (DFT) to study a diverse set of experimentally derived metal–organic frameworks (MOFs) with...

Metal-organic frameworks (MOFs) are a class of nanoporous materials with highly tunable structures in terms of both chemical composition and topology. Due to their tunable nature, high-throughput computational screening is a particularly appealing method to reduce the time-to-discovery of MOFs with desirable physical and chemical properties. In this work, a fully automated, high-throughput periodic density functional theory (DFT) workflow for screening promising MOF candidates was developed and benchmarked, with a specific focus on applications in catalysis. As a proof-of-concept, we use the high-throughput workflow to screen MOFs containing open metal sites (OMSs) from the Computation-Ready, Experimental MOF database for the oxidative C-H bond activation of methane. The results from the screening process suggest that, despite the strong C-H bond strength of methane, the main challenge from a screening standpoint is the identification of MOFs with OMSs that can be readily oxidized at moderate reaction conditions. © 2019 Wiley Periodicals, Inc.

Metal–organic frameworks (MOFs) are a widely investigated class of crystalline solids with tunable structures that make it possible to impart specific chemical functionality tailored for a given application. However, the enormous number of possible MOFs that can be synthesized makes it difficult to determine which materials would be the most promising candidates, especially for applications governed by electronic structure properties that are often computationally demanding to simulate and time-consuming to probe experimentally. Here, we have developed the first publicly available quantum-chemical database for MOFs (the “QMOF database”), which consists of properties derived from density functional theory (DFT) for over 14,000 experimentally synthesized MOFs. Throughout this study, we demonstrate how this new database can be used to identify MOFs with targeted electronic structure properties. As a proof-of-concept, we use the QMOF database to evaluate the performance of several machine learning models for the prediction of DFT-computed band gaps and find that crystal graph convolutional neural networks are capable of achieving superior predictive performance, making it possible to circumvent computationally expensive quantum-chemical calculations. We also show how unsupervised learning methods can aid the discovery of otherwise subtle structure–property relationships using the computational findings in this work. We conclude by highlighting several MOFs with low band gaps, a challenging task given the electronically insulating nature of most MOF structures. The data and predictive models generated in this work, as well as the database of MOF structures, should be highly useful to other researchers interested in the predictive design and discovery of MOFs for the many applications dictated by quantum-chemical phenomena.

Metal-organic frameworks (MOFs) with coordinatively unsaturated metal sites are appealing as adsorbent materials due to their tunable functionality and ability to selectively bind small molecules. Through the use of computational screening methods based on periodic density functional theory, we investigate O

Metal-organic frameworks (MOFs) have been widely investigated for challenging catalytic transformations due to their well-defined structures and high degree of synthetic tunability. These features, at least in principle, make MOFs ideally suited for a computational approach towards catalyst design and discovery. Nonetheless, the widespread use of data science and machine learning to accelerate the discovery of MOF catalysts has yet to be substantially realized. In this review, we provide an overview of recent work that sets the stage for future high-throughput computational screening and machine learning studies involving MOF catalysts. This is followed by a discussion of several challenges currently facing the broad adoption of data-centric approaches in MOF computational catalysis, and we share possible solutions that can help propel the field forward., 14 pages, 4 figures; to be published in Curr. Opin. Chem. Eng

A comprehensive set of surface phase diagrams addressing the catalytically relevant edges of the (100) surface of MoS2 catalysts is developed using dispersion-corrected density functional theory and ab initio thermodynamic modeling. The results of the temperature-dependent, free energy-based thermodynamic model are presented over the full range of catalytically relevant temperatures and pressures, in addition to S- and H-coverages ranging from 0 to 100%. The results of this work allow for a full thermodynamic analysis to be performed at the conditions relevant to any promising reaction involving MoS2, ranging from hydrodesulfurization to dehydrogenation to electrocatalysis. Several methodological recommendations are discussed and implemented with the goal of improving the accuracy of the surface phase diagrams at minimal computational expense. A library of the most stable S- and H-adsorption modes is also developed so that linear scaling relationships can be used to correlate thermodynamic stability with ...

Metal−organic frameworks (MOFs) with coordinatively unsaturated metal sites are appealing as adsorbent materials due to their tunable functionality and ability to selectively bind small molecules. Through the use of computational screening methods based on periodic density functional theory, we investigate O2 and N2 adsorption at the coordinatively unsaturated metal sites of several MOF families. A variety of design handles are identified that can be used to modify the redox activity of the metal centers, including changing the functionalization of the linkers (replacing oxido donors with sulfido donors), anion exchange of bridging ligands (considering μ-Br-, μ-Cl-, μ-F-, μ-SH-, or μ-OH- groups), and altering the formal oxidation state of the metal. As a result, we show that it is possible to tune the O2 affinity at the open metal sites of MOFs for applications involving the strong and/or selective binding of O2. In contrast with O2 adsorption, N2 adsorption at open metal sites is predicted to be relatively weak across the MOF dataset, with the exception of MOFs containing synthetically elusive V2+ open metal sites. As one example from the screening study, we predict that exchanging the μ-Cl- ligands of M2Cl2(BBTA) (H2BBTA = 1H,5H-benzo(1,2-d:4,5-d′)bistriazole) with μ-OH- groups would significantly enhance the strength of O2 adsorption at the open metal sites without a corresponding increase in the N2 affinity. Experimental investigation of Co2Cl2(BBTA) and Co2(OH)2(BBTA) confirms that the former exhibits only weak physisorption, whereas the latter is capable of chemisorbing O2 at room temperature. The chemisorption behavior is attributed to the greater electron-donating character of the μ-OH- ligands and the presence of H-bonding interactions between the μ-OH- bridging ligands and the O2 adsorbate.

Student evaluations of teaching are widely adopted across academic institutions, but there are many underlying trends and biases that can influence their interpretation. Publicly accessible web-based student evaluations of teaching are of particular relevance, due to their widespread use by students in the course selection process and the quantity of data available for analysis. In this study, data from the most popular of these websites, RateMyProfessors.com, is analysed for correlations between measures of instruction quality, easiness, physical attractiveness, discipline and gender. This study of 7,882,980 RateMyProfessors ratings (from 190,006 US professors with at least 20 student ratings) provides further insight into student perceptions of academic instruction and possible variables in student evaluations. Positive correlations were observed between ratings of instruction quality and easiness, as well as between instruction quality and attractiveness. On average, professors in science and e...

Polymorphism in metal−organic frameworks (MOFs) means that the same chemical building blocks (nodes and linkers) can be used to construct isomeric MOFs with different topological networks. The choice of topology can substantially impact the pore network of the MOF, changing the sizes and shapes of the pores, which has implications for adsorption and separation applications. In this work, we look at the influence of topology in 38 polymorphic MOFs on the separation of linear and branched C4–C6 alkane isomers, a separation of great importance to the petrochemical industry. We find that the MOF Cu2(1,4-benzenedicarboxylate) in nbo topology (nbo-Cu2BDC) has particularly high affinity for linear alkanes due to its small pore size, which excludes the branched isomers. Upon studying this MOF in further detail, we find that it can take either of two conformations: a cubic conformation, which is typical of nbo MOFs, and a unique star conformation that contains 1D triangular and hexagonal channels. The determination of which conformation the MOF will adopt depends on steric effects between the nodes and linkers.

Metal-organic frameworks (MOFs) with open metal sites have been widely investigated for the selective adsorption of small molecules via redox mechanisms where charge transfer can take place between the binding site and the adsorbate of interest. Quantum-chemical screening methods based on density functional theory have emerged as a promising route to accelerate the discovery of MOFs with enhanced binding affinities toward various adsorbates. However, the success of this approach is linked to the accuracy of the underlying density functional approximations (DFAs). In this work, we compare commonly used generalized gradient approximation (GGA), GGA+U, and meta-GGA exchange-correlation functionals in modeling redox-dependent binding at open metal sites in MOFs using O2 and N2 as representative small molecules. We find that the self-interaction error inherent to the widely used Perdew, Burke, and Ernzerhof (PBE) GGA predicts metal sites that are artificially redox-active, as evidenced by their strong binding affinities, short metal-adsorbate bond distances, and large degree of charge transfer. The incorporation of metal-specific, empirical Hubbard U corrections based on the transition metal oxide literature systematically reduces the redox activity of the open metal sites, often improving agreement with experiment. Additionally, the binding behavior shifts from strong chemisorption to weaker physisorption as a function of U. The M06-L meta-GGA typically predicts binding energies between those of PBE-D3(BJ) and PBE-D3(BJ)+U when using empirically derived U values from the transition metal oxide literature. Despite the strong sensitivity of the binding affinities toward a given DFA, the GGA, GGA+U, and meta-GGA approaches often yield the same qualitative trends and structure-property relationships.

Copper oxide catalysts supported on KIT-6 silica were evaluated for cyclohexane oxidative dehydrogenation (ODH) to determine the effects of copper oxide domain size on ODH activity and selectivity. The catalysts were prepared by incipient wetness impregnation of KIT-6 at copper surface densities spanning 0.01–0.7 Cu/nm2 with carefully controlled drying and calcination conditions to systematically vary the average local copper oxide domain size. A distinct copper oxide active site exhibiting an order of magnitude higher activity than large copper oxide domains was identified by model cyclohexane ODH studies coupled with in situ X-ray absorption and UV–visible spectroscopies during reduction in H2. The structure of this site is experimentally identified by a combination of extended X-ray absorption fine structure analysis, resonant Raman studies, and modeling by density functional theory. All constraints imposed by these techniques indicate the active site is a mono(μ-oxo)dicopper(II) structure with copper ...

X-ray imaging crystal spectroscopy (XICS) has been implemented on magnetic confinement fusion devices as a novel means of measuring local plasma temperature, impurity density, and flow profiles. At Alcator C-Mod, XICS allows for spatially-resolved, high spectral resolution measurements between 0.3 and 0.4 nm, enabling detailed analysis of He-like argon x-ray emission. Electron temperatures in the range of 0.5 keV ≤Te ≤3.0 keV are determined from He-like argon emissivity ratios of the n = 3 dielectronic satellites to the w-line and its surrounding n ≥ 3 satellites, specifically the wavelength range of 3.9440 A ≤ λ ≤ 3.9607 A. These data are validated against measurements of Te from existing electron cyclotron emission and Thomson scattering diagnostics. Line ratio data are analysed via a tomographic inversion procedure, overcoming the traditional issue of spectra being averaged over the plasma cross-section. The implications of utilizing x-ray line ratios as a valid local temperature diagnostic are not limited to Alcator C-Mod; properties of plasma in future experiments as well as in astrophysical settings can also be investigated. The results of this experiment confirm that x-ray line ratios can be used as an accurate electron temperature diagnostic. The electron temperature can be determined from the relation to the line ratio, x, as Te[keV] = 0.1552x−0.7781 with 0.0223 < x < 0.2449.

A new method is presented to calculate binding energies and eigenfunctions for molecules, using the Dirac–Slater Hamiltonian. A numerical basis set of four component wavefunctions is obtained from atom‐like Dirac–Slater wavefunctions. A discrete variational method (DVM) has been applied to generate the binding energies and eigenfunctions for the molecule. Results are given for a series of molecules, including dihydrides H2X (X=O, S, Se, Te), diatomic indium halides InX (X=F, Cl, Br, I), and metal chlorides XCl (X=B, Al, Ga, In, Tl). Comparison is made with results from nonrelativistic calculations using the DVM with numerical Hartree–Fock–Slater‐type wavefunctions and with other types of nonrelativistic calculations. In particular, relativistic level shifts and spin–orbit splitting have been analyzed. The theoretical ionization energies are compared with experimental results. Generally a very good agreement is obtained between the experimental and theoretical binding energies for the valence levels, calcu...

A numerical-variational method for performing self-consistent molecular calculations in the Hartree-Fock-Slater (HFS) model is presented. Molecular wavefunctions are expanded in terms of basis sets constructed from numerical HFS solutions of selected one-center atomlike problems. Binding energies and wavefunctions for the molecules are generated using a discrete variational method for a given molecular potential. In the self-consistent-charge (SCC) approximation to the complete self-consistent-field (SCF) method, results of a Mulliken population analysis of the molecular eigenfunctions are used in each iteration to produce 'atomic' occupation numbers. The simplest SCC potential is then obtained from overlapping spherical atomlike charge distributions. Molecular ionization energies are calculated using the transition-state procedure; results are given for CO, H2O, H2S, AlCl, InCl, and the Ni5O surface complex. Agreement between experimental and theoretical ionization energies for the free-molecule valence levels is generally within 1 eV. The simple SCC procedure gives a reasonably good approximation to the molecular potential, as shown by comparison with experiment, and with complete SCF calculations for CO, H2O, and H2S.