Metal-organic frameworks (MOFs) are a class of crystalline materials composed of metal nodes or clusters connected via semi-rigid organic linkers. Owing to their high surface area, porosity, and tunability, MOFs have received significant attention for numerous applications such as gas separation and storage. Atomistic simulations and data-driven methods (e.g., machine learning) have been successfully employed to screen large databases and successfully develop new experimentally synthesized and validated MOFs for CO2 capture. To enable data-driven materials discovery for any application, the first (and arguably most crucial) step is database curation. This work introduces the ab initio REPEAT charge MOF (ARC-MOF) database. This is a database of ~280,000 MOFs which have been either experimentally characterized or computationally generated, spanning all publicly available MOF databases. A key feature of ARC-MOF is that it contains DFT-derived electrostatic potential fitted partial atomic charges for each MOF. Additionally, ARC-MOF contains pre-computed descriptors for out-of-the-box machine learning applications. An in-depth analysis of the diversity of ARC-MOF with respect to the currently mapped design space of MOFs was performed – a critical, yet commonly overlooked aspect of previously reported MOF databases. Using this analysis, balanced subsets from ARC-MOF for various machine learning purposes have been identified. Other chemical and geometric diversity analyses are presented, with an analysis on the effect of charge assignment method on atomistic simulation of gas uptake in MOFs.

n response to Dr. Clivio's comments.Dr. Clivio's comments correctly point out that the choice of a particular stereoisomer shown as an illustration in Fig. 4c in our original manuscript 1 was unfortunate, as we selected an isomer that turns out to be inconsistent with what the experimental data suggested.We had carried out some preliminary density functional theory (DFT) calculations to investigate the formation of the four different stereoisomers within the MOF pores (see Fig. 1 below).These calculations showed that the transsyn (A) has the lowest energy (-24.5 kcal/mol), while the other isomers have energy in the range -10 to 13 kcal/mol, suggesting that experimentally one might preferentially form the trans-syn (A).These DFT calculations were performed at 0 K, while the experiments were done at room temperature.The minimum energy conformation of adenine-thymine bonding at 0 K would provide us with limited information about the behavior of thymine in the pores of the MOF at room temperature, which is why we turned to molecular dynamic simulations (MD) at room temperature.The results shown in Fig. 3c,d in our original manuscript 1 are from MD simulations.These MD simulations were not restricted to one particular isomer.We agree with Dr. Clivio that if we would have limited our calculations to only the structure shown in Fig. 4c in the original manuscript 1 would have introduced a bias, but in our MD simulations, we did not.We concluded that the experimental data, while able to confirm the dimer formation, were unable to define whether a

This is a database of ~280,000 MOFs which have been either experimentally characterized or computationally generated, spanning all publicly available MOF databases. DFT-derived REPEAT charges, adsorption data, and various descriptors are available for all MOFs. all_structures_1.tar.gz and all_structures_2.tar.gz – these are the cif files that were considered to compose the “entire known design space” of MOFs, with any bad structures removed (split into two separate tarballs since it is a lot of data). ARCMOF_20220610.tar.gz – these are all of the cif files with REPEAT charges composing ARC-MOF. flig-clusters.csv, func-clusters.csv, geo-clusters.csv, mc-clusters.csv – Each file indicates for each MOF which cluster it belongs to, and whether the MOF is present in ARC-MOF. This is done for each "type" of MOF chemistry and for the geometric properties. Clusters with a negative value indicate the MOF does not belong to any cluster (i.e., it is assumed to be "unique"). all_topology_lists.csv – a csv file containing the topology reported by the filename of applicable structures, and the topology reported by CrystalNets.jl ML_test_set.tar.gz – these are the cif files (with REPEAT charges) of the MOFs in the diverse-mc subset, but missing from ARC-MOF (for the purposes of a ML test set for the prediction of metal charges). geometric_properties.csv – a csv file containing geometric descriptors computed for this study for all MOFs. The csv file also indicates which MOFs are present in ARC-MOF, and the order in which they were chosen for the farthest point sampling (up to 100K MOFs). RACs.csv – See geometric_properties.csv description. Same type of file, but with the RAC descriptors. RDFs.csv – The RDFs for each MOF, using several atomic properties. Some atomic properties are not available for all elements. In the cases where the atomic property is not available for a particular structure, no value is assigned. methane.csv, methane_purification-CH4.csv, methane_purification_CO2.csv, post_comb_vsa-CO2.csv, post_comb_vsa-N2.csv, pre_comb_4040-CO2.csv, pre_comb_4040-H2.csv, landfill-CH4.csv, landfill-CO2.csv – these are csv files of the raw uptake data and various temperature, pressure conditions (with standard deviations) for each gas separation process specified in the file overall_process.csv. overall_process.csv – This is a csv file of the adsorption properties of the MOFs. Particularly, the csv files contain the working capacity (mmol/g_working_capacity) and selectivity of each MOF for each of the five process conditions. mc-diverse-set.csv, func-diverse-set.csv – csv files containing which MOFs are present in each diverse set (from farthest point sampling of the MOFs based on either their functional group chemistry or metal chemistry). The file indicates which MOFs are present in ARC-MOF and which are not. Version history of repository: v2 -- added file: "all_topology_lists.csv" v3 -- added file: "ML_test_set.tar.gz" v4 -- replaced file: "ML_test_set.tar.gz". Originally incorrect repository of cifs

By combining metal nodes and organic linkers one can make millions of different metal-organic frameworks (MOFs). At present over 90,000 MOFs have been synthesized and there are databases with over 500,000 predicted structures. This raises the question whether a new experimental or predicted structure adds new information. For MOF-chemists the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we show how machine learning can be used to quantify similarities of MOFs. This quantification allows us to use techniques from ecology to analyse the chemical diversity of these materials in terms of diversity metrics. In particular, we show that this diversity analysis can identify biases in the databases, and how such bias can lead to incorrect conclusions. This formalism provides us with a simple and powerful practical guideline to see whether a set of new structures will have the potential for new insights, or constitute a relatively small variation of existing structures.

Metal-organic frameworks (MOFs) have garnered interest as potential solid sorbent materials for postcombustion CO2 capture. With a seemingly infinite design space, high-throughput computational screening of MOFs has developed into an effective tool for the development of new materials. In this work, machine learning (ML) has been used to develop accurate quantitative structure-property relationship (QSPR) models to rapidly predict the CO2 working capacity and CO2/N-2 selectivity at the low-pressure conditions relevant to postcombustion carbon capture (0.15 bar CO2, 0.85 bar N-2). A database of over 340 000 MOFs constructed from hundreds of types of building units arranged in over 1000 net topologies was used to train and test the models. Neural network ML models were optimized using six geometric descriptors along with three so-called chemical descriptors, namely, the atomic property-weighted radial distribution function (AP-RDF) and some variants thereof, the bag-of-atoms, and the chemical motif density descriptors. The ML models built using geometric descriptors alone resulted in test set correlation R-2 values of only 0.71 and 0.75 for CO2 working capacity and CO2/N-2 selectivity, respectively. ML models built with a single type of chemical descriptor all outperformed the geometry-only models giving R-2 values ranging from 0.83 to 0.94 with the AP-RDF model being the most accurate. Overall, the best model was built using a combination of AP-RDF, chemical motif, and geometric descriptors (R-2 = 0.96 when predicting the CO2 working capacity and R-2 = 0.95 for the selectivity). To date, these are the most accurate ML models for predicting low-pressure gas uptake of MOFs. The combined model was able to capture 994 of the true top 1000 MOFs (from a test set of similar to 70 000) within the top 5000 MOFs as predicted by the model with CO, working capacity as the target. Thus, if the ML model were used to prescreen materials for more compute intensive GCMC simulations, then it would result in a greater than 10 times speed up while still capturing >99% of high-performing materials. These results highlight the importance of chemical descriptors in predicting low-pressure gas adsorption properties in nanoporous materials.

Biologically derived metal-organic frameworks (bio-MOFs) are of great importance as they can be used as models for bio-mimicking and in catalysis, allowing us to gain insights into how large biological molecules function. Through rational design, here we report the synthesis of a novel bio-MOF featuring unobstructed Watson-Crick faces of adenine (Ade) pointing towards the MOF cavities. We show, through a combined experimental and computational approach, that thymine (Thy) molecules diffuse through the pores of the MOF and become base-paired with Ade. The Ade-Thy pair binding at 40-45% loading reveals that Thy molecules are packed within the channels in a way that fulfill both the Woodward-Hoffmann and Schmidt rules, and upon UV irradiation, Thy molecules dimerize into Thy<>Thy. This study highlights the utility of accessible functional groups within the pores of MOFs, and their ability to 'lock' molecules in specific positions that can be subsequently dimerized upon light irradiation, extending the use of MOFs as nanoreactors for the synthesis of molecules that are otherwise challenging to isolate.

Limiting the increase of CO2 in the atmosphere is one of the largest challenges of our generation(1). Because carbon capture and storage is one of the few viable technologies that can mitigate current CO2 emissions(2), much effort is focused on developing solid adsorbents that can efficiently capture CO2 from flue gases emitted from anthropogenic sources(3). One class of materials that has attracted considerable interest in this context is metal-organic frameworks (MOFs), in which the careful combination of organic ligands with metal-ion nodes can, in principle, give rise to innumerable structurally and chemically distinct nanoporous MOFs. However, many MOFs that are optimized for the separation of CO2 from nitrogen(4-7) do not perform well when using realistic flue gas that contains water, because water competes with CO2 for the same adsorption sites and thereby causes the materials to lose their selectivity. Although flue gases can be dried, this renders the capture process prohibitively expensive(8,9). Here we show that data mining of a computational screening library of over 300,000 MOFs can identify different classes of strong CO2-binding sites-which we term `adsorbaphores'-that endow MOFs with CO2/N-2 selectivity that persists in wet flue gases. We subsequently synthesized two water-stable MOFs containing the most hydrophobic adsorbaphore, and found that their carbon-capture performance is not affected by water and outperforms that of some commercial materials. Testing the performance of these MOFs in an industrial setting and consideration of the full capture process-including the targeted CO2 sink, such as geological storage or serving as a carbon source for the chemical industry-will be necessary to identify the optimal separation material.

: We report the syntheses and structures of fi ve metal − organic frameworks (MOFs) based on transition metals (Ni II , Cu II , and Zn II ), adenine, and di-, tri-, and tetracarboxylate ligands. Adenine, with multiple N donor sites, was found to coordinate to the metal centers in di ff erent binding modes including bidentate (through N7 and N9, or N3 and N9) and tridentate (through N3, N7, and N9). Systematic investigations of the protonation states of adenine in each MOF structure via X-ray photoelectron spectroscopy revealed that adenine can be selectively protonated through N1, N3, or N7. The positions of H atoms connected to the N atoms were found from the electron density maps, and further supported by the study of C − N − C bond angles compared to the literature reports. DFT calculations were performed to geometrically optimize and energetically assess the structures simulated with di ff erent protonation modes. The present study highlights the rich coordination chemistry of adenine and provides a method for the determination of its protonation states and the location of protonated N atoms of adenine within MOFs, a task that would be challenging in complicated adenine-based calibrated regularly to set the Fermi edge of a silver reference sample at zero binding energy. Sample charging was compensated for using an electron fl ood gun. The binding energy scale was referenced using the adventitious carbon C 1s peak which was set to 284.6 eV. Powder samples were prepared by pressing into indium foil, with care taken to ensure that no In signal was observed in the resulting spectra. Other Characterizations. Thermogravimetric analyses (TGA) for SION-31 , SION-32 , SION-33 , SION-34 , and SION-35 were performed under an air atmosphere using a thermogravimetric instrument SDT Q600. The heating rate was set to 5 ° C/min until 600 ° C, and then the samples were cooled to room temperature at a rate of 10 ° C/min. Elemental analysis results (C, H, and N) were obtained using a Thermo EA1112 Flash CHNS-O Analyzer.

We have developed a simple text mining algorithm that allows us to identify surface area and pore volumes of metal-organic frameworks (MOFs) using manuscript html files as inputs. The algorithm searches for common units (e.g., m2/g, cm3/g) associated with these two quantities to facilitate the search. From the sample set data of over 200 MOFs, the algorithm managed to identify 90% and 88.8% of the correct surface area and pore volume values. Further application to a test set of randomly chosen MOF html files yielded 73.2% and 85.1% accuracies for the two respective quantities. Most of the errors stem from unorthodox sentence structures that made it difficult to identify the correct data as well as bolded notations of MOFs (e.g., 1a) that made it difficult identify its real name. These types of tools will become useful when it comes to discovering structure-property relationships among MOFs as well as collecting a large set of data for references.

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.

Metal-organic frameworks (MOFs) have emerged as versatile materials for applications ranging from gas separation and storage, catalysis, and sensing. The attractive feature of MOFs is that, by changing the ligand and/or metal, they can be chemically tuned to perform optimally for a given application. In most, if not all, of these applications one also needs a material that has a sufficient mechanical stability, but our understanding of how changes in the chemical structure influence mechanical stability is limited. In this work, we rationalize how the mechanical properties of MOFs are related to framework bonding topology and ligand structure. We illustrate that the functional groups on the organic ligands can either enhance the mechanical stability through formation of a secondary network of nonbonded interactions or soften the material by destabilizing the bonded network of a MOF. In addition, we show that synergistic effect of the bonding network of the material and the secondary network is required to achieve optimal mechanical stability of a MOF. The developed molecular insights in this work can be used for systematic improvement of the mechanical stability of the materials by careful selection of the functional groups.

Charge equilibration (Qeq) methods can estimate the electrostatic potential of molecules and periodic frameworks by assigning point charges to each atom, using only a small fraction of the resources needed to compute density functional (DFT)-derived charges. This makes possible, for example, the computational screening of thousands of microporous structures to assess their performance for the adsorption of polar molecules. Recently, different variants of the original Qeq scheme were proposed to improve the quality of the computed point charges. One focus of this research was to improve the gas adsorption predictions in metal-organic frameworks (MOFs), for which many different structures are available. In this work, we review the evolution of the method from the original Qeq scheme, understanding the role of the different modifications on the final output. We evaluated the result of combining different protocols and set of parameters, by comparing the Qeq charges with high quality DFT-derived DDEC charges for 2338 MOF structures. We focused on the systematic errors that are attributable to specific atom types to quantify the final precision that one can expect from Qeq methods in the context of gas adsorption where the electrostatic potential plays a significant role, namely, CO2 and H2S adsorption. In conclusion, both the type of algorithm and the input parameters have a large impact on the resulting charges, and we draw some guidelines to help the user to choose the proper combination of the two for obtaining a meaningful set of charges. We show that, considering this set of MOFs, the accuracy of the original Qeq scheme is often still comparable with the most recent variants, even if it clearly fails in the presence of certain atom types, such as alkali metals.

In this work, we report the synthesis of SION-8, a novel metal-organic framework (MOF) based on Ca(II) and a tetracarboxylate ligand TBAPy4- endowed with two chemically distinct types of pores characterized by their hydrophobic and hydrophilic properties. By altering the activation conditions, we gained access to two bulk materials: the fully activated SION-8F and the partially activated SION-8P with exclusively the hydrophobic pores activated. SION-8P shows high affinity for both CO2 ( Qst = 28.4 kJ/mol) and CH4 ( Qst = 21.4 kJ/mol), while upon full activation, the difference in affinity for CO2 ( Qst = 23.4 kJ/mol) and CH4 ( Qst = 16.0 kJ/mol) is more pronounced. The intrinsic flexibility of both materials results in complex adsorption behavior and greater adsorption of gas molecules than if the materials were rigid. Their CO2/CH4 separation performance was tested in fixed-bed breakthrough experiments using binary gas mixtures of different compositions and rationalized in terms of molecular interactions. SION-8F showed a 40-160% increase (depending on the temperature and the gas mixture composition probed) of the CO2/CH4 dynamic breakthrough selectivity compared to SION-8P, demonstrating the possibility to rationally tune the separation performance of a single MOF by manipulating the stepwise activation made possible by the MOF's biporous nature.

We report the syntheses and structures of five metal-organic frameworks (MOFs) based on transition metals (NiII, CuII, and ZnII), adenine, and di-, tri-, and tetra-carboxylate ligands. Adenine, with multiple N donor sites, was found to coordinate to the metal centers in different binding modes including bidentate (through N7 and N9, or N3 and N9) and tridentate (through N3, N7, and N9). Systematic investigations of the protonation states of adenine in each MOF structure via X-ray photoelectron spectroscopy revealed that adenine can be selectively protonated through N1, N3, or N7. The positions of H atoms connected to the N atoms were found from the electron density maps, and further supported by the study of C-N-C bond angles compared to the literature reports. DFT calculations were performed to geometrically optimize and energetically assess the structures simulated with different protonation modes. The present study highlights the rich coordination chemistry of adenine and provides a method for the determination of its protonation states and the location of protonated N atoms of adenine within MOFs, a task that would be challenging in complicated adenine-based MOF structures.

Pore volume is one of the main properties for the characterization of microporous crystals. It is experimentally measurable, and it can also be obtained from the refined unit cell by a number of computational techniques. In this work, we assess the accuracy and the discrepancies between the different computational methods which are commonly used for this purpose, i.e, geometric, helium, and probe center pore volumes, by studying a database of more than 5000 frameworks. We developed a new technique to fully characterize the internal void of a microporous material and to compute the probe-accessible and-occupiable pore volume. We show that, unlike the other definitions of pore volume, the occupiable pore volume can be directly related to the experimentally measured pore volumes from nitrogen isotherms.

For applications of metal-organic frameworks (MOFs) such as gas storage and separation, flexibility is often seen as a parameter that can tune material performance. In this work we aim to determine the optimal flexibility for the shape selective separation of similarly sized molecules (e.g., Xe/Kr mixtures). To obtain systematic insight into how the flexibility impacts this type of separation, we develop a simple analytical model that predicts a material's Henry regime adsorption and selectivity as a function of flexibility. We elucidate the complex dependence of selectivity on a framework's intrinsic flexibility whereby performance is either improved or reduced with increasing flexibility, depending on the material's pore size characteristics. However, the selectivity of a material with the pore size and chemistry that already maximizes selectivity in the rigid approximation is continuously diminished with increasing flexibility, demonstrating that the globally optimal separation exists within an entirely rigid pore. Molecular simulations show that our simple model predicts performance trends that are observed when screening the adsorption behavior of flexible MOFs. These flexible simulations provide better agreement with experimental adsorption data in a high-performance material that is not captured when modeling this framework as rigid, an approximation typically made in high-throughput screening studies. We conclude that, for shape selective adsorption applications, the globally optimal material will have the optimal pore size/chemistry and minimal intrinsic flexibility even though other nonoptimal materials' selectivity can actually be improved by flexibility. Equally important, we find that flexible simulations can be critical for correctly modeling adsorption in these types of systems.

In this work, MOF bulk properties are evaluated and compared using several force fields on several well-studied MOFs, including IRMOF-1 (MOF-5), IRMOF-10, HKUST-1, and UiO-66. It is found that, surprisingly, UFF and DREIDING provide good values for the bulk modulus and linear thermal expansion coefficients for these materials, excluding those that they are not parametrized for. Force fields developed specifically for MOFs including UFF4MOF, BTW-FF, and the DWES force field are also found to provide accurate values for these materials’ properties. While we find that each force field offers a moderately good picture of these properties, noticeable deviations can be observed when looking at properties sensitive to framework vibrational modes. This observation is more pronounced upon the introduction of framework charges.

Understanding how crystalline materials are assembled is important for the rational design of metal-organic frameworks (MOFs), through streamlining their synthesis and controlling their properties for targeted applications. Herein, we report for the first time the construction of two 3-dimensional Tb(III) based MOFs; a metastable MOF acting as an intermediate phase, that partially dissolves and transforms into a chemically and thermodynamically stable MOF. This chemical transformation occurs solely in a N,N-dimethylformamide/water solvent mixture, and is triggered when additional energy is provided to the reaction. In situ studies reveal the partial dissolution of the metastable phase after which the MOF components are reassembled into the thermodynamically stable phase. The marked difference in thermal and chemical stability between the kinetically and thermodynamically controlled phases is contrasted by their identical chemical building unit composition.

There is currently a push towards big data and data mining in materials research to accelerate discovery. Zeolites, metal–organic frameworks and other related crystalline porous materials are not immune to this phenomenon, as evidenced by the proliferation of porous structure databases and computational gas-adsorption screening studies over the past decade. The endeavour to identify the best materials for various gas separation and storage applications has led not only to thousands of synthesized structures, but also to the development of algorithms for building hypothetical materials. The materials databases assembled with these algorithms contain a much wider range of complex pore structures than have been synthesized, with the reasoning being that we have discovered only a small fraction of realizable structures and expanding upon these will accelerate rational design. In this Review, we highlight the methods developed to build these databases, and some of the important outcomes from large-scale computational screening studies. The discovery of nanoporous materials is now being propelled by the analysis of big data combined with traditional computational thermodynamics calculations. In this Review, we analyse the current state of the art, with a focus on the generation of computational databases and results from large-scale screening for gas separations.

Here we present a method for constructing hypothetical crystalline nanoporous materials, such as metalorganic frameworks (MOFs), using a graph theoretical approach. The method takes as input the discrete secondary (or structural) building units (SBUs) with defined connection points, and a desired 3-dimensional net topology in the form of a labelled quotient graph. The hypothetical materials are constructed based on the principle that using a labelled quotient graph obtained, for example from the reticular chemistry structure resource (RCSR), one can construct a net embedding in 3-D Euclidean space with an infinite number of different representations. Thus, crystalline structures can be realized by manipulating a net's embedding such that vertices of the net match the geometries of the desired SBUs. To demonstrate the methodology, 46 different network topologies (i.e. tbo, pcu), are used to build MOFs from the same pair of 4-coordinate and 3-coordinate SBUs. We further show that the method can be used to generate hypothetical MOFs where the most common realization of a net, called the barycentric representation, will not produce a viable structure. When combined with a robust force field based geometry optimizer, the method can be used to generate large and structurally diverse hypothetical databases for virtual screening purposes.