Metal-organic Frameworks Database Research Articles

Accelerating the practical application of MOFs for hydrogen storage—from performance-driven to application-oriented

Metal–organic frameworks (MOFs) are highly promising porous materials known for their exceptional porosity, extensive surface area, and customizable pore structures, making them an ideal solution for hydrogen storage. However, most MOFs research remains confined to the laboratory, lacking practical applications. To address this, the author proposes a shift towards practical applications, the creation of a comprehensive MOFs database, alignment of synthesis with practical considerations, and diversification of MOFs applications. These steps are crucial for harnessing the full potential of MOFs in real-world energy challenges.

Machine Learning-Assisted Discovery of Propane-Selective Metal-Organic Frameworks.

Machine learning is gaining momentum in the prediction and discovery of materials for specific applications. Given the abundance of metal-organic frameworks (MOFs), computational screening of the existing MOFs for propane/propylene (C3H8/C3H6) separation could be equally important for developing new MOFs. Herein, we report a machine learning-assisted strategy for screening C3H8-selective MOFs from the CoRE MOF database. Among the four algorithms applied in machine learning, the random forest (RF) algorithm displays the highest degree of accuracy. We experimentally verified the identified top-performing MOF (JNU-90) with its benchmark selectivity and separation performance of directly producing C3H6. Considering its excellent hydrolytic stability, JNU-90 shows great promise in the energy-efficient separation of C3H8/C3H6. This work may accelerate the development of MOFs for challenging separations.

High-throughput computational screening of MOF adsorbents for efficient propane capture from air and natural gas mixtures.

In this study, we used a high-throughput computational screening approach to examine the potential of metal-organic frameworks (MOFs) for capturing propane (C3H8) from different gas mixtures. We focused on Quantum MOF (QMOF) database composed of both synthesized and hypothetical MOFs and performed Grand Canonical Monte Carlo (GCMC) simulations to compute C3H8/N2/O2/Ar and C3H8/C2H6/CH4 mixture adsorption properties of MOFs. The separation of C3H8 from air mixture and the simultaneous separation of C3H8 and C2H6 from CH4 were studied for six different adsorption-based processes at various temperatures and pressures, including vacuum-swing adsorption (VSA), pressure-swing adsorption (PSA), vacuum-temperature swing adsorption (VTSA), and pressure-temperature swing adsorption (PTSA). The results of molecular simulations were used to evaluate the MOF adsorbents and the type of separation processes based on selectivity, working capacity, adsorbent performance score, and regenerability. Our results showed that VTSA is the most effective process since many MOFs offer high regenerability (>90%) combined with high C3H8 selectivity (>7 × 103) and high C2H6 + C3H8 selectivity (>100) for C3H8 capture from air and natural gas mixtures, respectively. Analysis of the top MOFs revealed that materials with narrow pores (<10Å) and low porosities (<0.7), having aromatic ring linkers, alumina or zinc metal nodes, typically exhibit a superior C3H8 separation performance. The top MOFs were shown to outperform commercial zeolite, MFI for C3H8 capture from air, and several well-known MOFs for C3H8 capture from natural gas stream. These results will direct the experimental efforts to the most efficient C3H8 capture processes by providing key molecular insights into selecting the most useful adsorbents.

Pore Structure Compartmentalization for Advanced Characterization of Metal-Organic Framework Materials.

Metal-organic frameworks (MOFs) are nanoporous crystals which are widely used as selective adsorbents, separation membranes, catalysts, gas and energy storage media, and drug delivery vehicles. The unique adsorption and transport properties of MOFs are determined by their complex three-dimensional (3D) networks of pores, cages, and channels that differ in size, shape, and chemical composition. While the morphological structure of MOF crystals is known, practical MOF materials are rarely ideal crystals. They contain secondary phases, binders, residual chemicals, and various types of defects. It is of paramount importance to evaluate the degree of crystallinity and accessibility of different pore compartments to adsorb guest molecules. To this end, we recently suggested the method of fingerprint isotherms based on the comparison of the experimentally measured adsorption isotherms and theoretical isotherms on ideal MOF crystals produced by Monte Carlo (MC) simulations and decomposed with respect to different pore compartments [Parashar, S. ACS Appl. Nano Mater. 2021, 4, 5531-5540 and Dantas, S.; Neimark, A. V. ACS Appl. Mater. Interfaces 2020, 12, 15595-15605]. In this work, we develop an automated algorithm for pore network compartmentalization that is a prerequisite for calculations of the fingerprint isotherms. The proposed algorithm partitions the unit cell into realistically shaped compartments based on the geometric pore size distribution. The proposed method is demonstrated on several characteristic systems, including Cu-BTC, IRMOF-1, UiO-66, PCN-224, ZIF-412, and 56 structures from the CoRE MOF database.

Machine learning aided computational exploration of metal–organic frameworks with open Cu sites for the effective separation of hydrogen isotopes

Efficient separation of hydrogen isotopes is of vital importance to develop nuclear energy industry, while it remains a significant challenge to separate D2 from H2 due to their identical physicochemical properties. As one of the efficient alternatives to conventional techniques, the thermodynamic quantum sieving technology using metal–organic frameworks (MOFs) featuring open metal sites (OMSs) has shown a great potential. However, the lack of transferable force fields in conventional molecular simulations and high expense of brute-force screening hinder the quick discovery of MOFs targeted for D2/H2 separation. Herein, based on the established force field with high accuracy and transferability, machine learning and feature engineering are applied to address these challenges. Machine learning comprehensively assesses different descriptors that influence the separation performance of 929 experimentally-reported MOFs bearing Cu(II)-OMS. By employing the same metal nodes, new Cu MOF database (6,748 MOFs) is constructed, in which 45 hypothetical MOFs are firstly identified out through feature engineering that exhibiting high performance. Furthermore, grand canonical Monte Carlo simulations are performed on these MOFs, among which the optimal one exhibits comparable selectivity (36.9) and high adsorbent performance score (315.9) that surpasses the state-of-the-art materials do. This work not only presents a cost-effective approach firstly applying in the separation of hydrogen isotopes, but also provides experimental guidance for the design of high-performance adsorbents.

High-Throughput Computational Screening and Machine Learning Model for Accelerated Metal–Organic Frameworks Discovery in Toluene Vapor Adsorption

Some hazardous gases, like toluene vapor, have caused serious environmental pollution. The adsorption of toluene using metal–organic frameworks (MOFs) has been considered a useful mechanism to reduce environmental pollution. High-throughput computation using the grand canonical Monte Carlo (GCMC) approach was used to screen high-performance MOFs from the CoRE MOF database. A total of 802 MOFs are selected with a toluene uptake larger than HKUST-1 (6.34 mmol/g at 1900 Pa, 298 K) and CMOF-3b ([(L3b)Cu2]n, L3b = 4,4′,4″,4‴-(2,2′-dihydroxy-1,1′-binaphthalene-4,4′,6,6′-tetrayl)), showing the highest toluene vapor adsorption capacity (25.57 mmol/g). Approximately 80% of high-performance MOFs contain open metal sites. Further analyses of the quantitative structure–property relationships reveal that the MOF adsorption capacity for toluene could be primarily correlated with gravimetric surface area and void fraction. Moreover, using the HKUST-1 as a template, center-metal element replacement is suggested to be effective in improving toluene vapor adsorption. Finally, based on our previously proposed MOF-CGCNN algorithm, a regression model is developed to predict toluene adsorption capacity. Combined with high-throughput GCMC calculation, the machine learning model is applied to screen a larger MOF database (containing 137,953 hypothetical MOFs), which accelerates the virtual discovery of new high-performance candidate MOFs for toluene adsorption. The proposed strategy will be useful in material design or discovery for reducing toluene, thereby benefitting the environment.

Analyzing of metal organic frameworks performance in CH4 adsorption using machine learning techniques: A GBRT model based on small training dataset

Metal organic frameworks (MOFs) have already been widely used in the field of gas adsorption and separation. The combination of various organic ligands and metal ions results in large MOF libraries with different topological structures. However, such large structural databases make it difficult to screen out MOF materials with high adsorption performance towards a given gas to establish statistical pictures through experiments or molecular simulations. In this work, we studied and built machine learning (ML) models based on the hypothetical MOF (hMOF) database. A gradient-boosted regression trees (GBRT) model with 1000 samples can predict the methane adsorption capacity of more than 130,000 hMOFs with high accuracy. A fast high-throughput screening on the entire database was performed using the constructed regression model, which extracts several molecular fragments from the top 1% of materials with the largest methane adsorption capacity. Careful analysis of the chemical structure of these fragments indicates that MOF materials containing cyano, hydroxyl, carboxyl and/or aromatic rings may possess high adsorption performance. The electrostatic potential distribution of these fragments was obtained at the ab initio computational level and was highlighted to be another important descriptor. We further extended the GBRT predictor to diverse MOF libraries including tobacco MOF (tobmof) and CoRE MOF. The molecular fragments extracted from the ML-based screening on the real CoRE MOF database as well as the corresponding electrostatic properties were compared with the results of hMOFs, which provides new insight into the design of MOF materials.

Expanding Linker Dimensionality in Metal-organic Frameworks for sub-Ångstrom Pore Control for Separation Applications.

Metal-organic frameworks (MOFs) are a class of porous materials with high surface areas, which are acquiring rapid attention on an exponential basis. A significant characteristic of MOFs is their ability to act as adsorbents to selectively separate component mixtures of similar size, thereby addressing the technological need for an alternative approach to conventional distillation methods. Recently, MOFs comprising a 3-Dimensional (3D) linker have shown outstanding capabilities for difficult separations compared to the parent 2-Dimensional (2D) analogue. 3D-linkers with a polycyclic core are underrepresented in the MOF database due to the widespread preferred use of 2D-linkers and the misconceived high-cost of 3D linkers. We summarize the recent research of 3D-linker MOFs and highlight their beneficial employment for selective gas and hydrocarbon adsorption and separation. Furthermore, we outline forecasts in this area to create a platform for widespread adoption of 3D-linkers in MOF synthesis.

Theoretical Design of MOFs and PSA Process for Efficient Separation of CF4/NF3

Nitrogen trifluoride (NF3) is a kind of electronic specialty gas used as an etchant for the production of semiconductor chips. However, trace amounts of CF4 impurities in the NF3 product have seriously limited its application. In this work, high-throughput calculations were used to efficiently screen the metal–organic frameworks (MOFs) with excellent CF4-selective adsorption performance based on the 796 MOFs from the CoRE MOF database and 10 top-performing materials were identified, in which JUCXEK stands out with the highest CF4 selectivity of 50.15. We designed JUCXEK material by changing the metal sites, and Zr-JUCXEK was found to be the most promising material used in industry. The pressure swing adsorption (PSA) process simulation using Zr-JUCXEK as the adsorbent was further designed to verify its industrial values. The CF4 outlet concentration and NF3 recovery meet the industrial requirement, indicating that Zr-JUCXEK possesses the ability to remove CF4 and obtain high-quality NF3 products.

Identifying High-Performance Metal–Organic Frameworks for Low-Temperature Oxygen Recovery from Helium by Computational Screening

Metal–organic frameworks (MOFs) are an important class of porous crystalline materials for applications ranging from gas adsorption and separation to catalysis. There are thousands of potential MOFs available for separation applications. We developed a computational approach to screen MOFs for the separation of oxygen–helium mixtures at low temperatures (100–200 K), conditions that were motivated by issues associated with propulsion in space-based settings. We used detailed molecular simulations for a small number of MOFs to develop screening methods that were then used to estimate the optimum temperatures for separations using pressure swing adsorption for 2932 MOFs from the CoRE MOF database and the swing capacity and oxygen–helium selectivity at these temperatures. We used the stability of the best-performing structures in the presence of moisture as a means to provide a short list of high-performance materials. In addition to identifying specific materials for oxygen–helium separations, this approach could prove useful for selecting adsorbents for other gas separations.

High-Throughput Screening of Metal–Organic Frameworks Assisted by Machine Learning: Propane/Propylene Separation

The separation of a propane (C3H8)/propylene(C3H6) mixture is of paramount importance in the petrochemical industry. Metal–organic frameworks (MOFs), as a class of promising alternative to the traditional adsorbents, have garnered extensive interest. This study proposes a machine learning-assisted high-throughput screening strategy for the identification of suitable MOFs for C3H8/C3H6 separation, striving to accelerate the discovery of high-performance MOF candidates for this particular application. First, a chemical/geometric analysis-based prescreening is applied to a data set of 146 203 MOFs composed of an experimentally synthesized MOF database and a hypothetical MOF database, and MOFs with undesirable chemical/geometric features were excluded. Six structural and nine chemical descriptors were calculated for the remaining MOFs. Random Forest regression algorithm was applied to “learn” the relationship correlations between the features (chemical and/or structural) of MOFs and their C3H8/C3H6 separation capacity. Grand Canonical Monte Carlo (GCMC) simulations were applied to evaluate the C3H8/C3H6 separation performances of the randomly selected training and testing MOF samples. A performance prediction model based on chemical and structural descriptors was obtained with R2 equal to 0.96, which was employed for a separation performance prediction of the remaining MOFs. 2500 MOFs with potential to possess high C3H8/C3H6 separation performance were shortlisted by the prediction model. GCMC simulations were applied to calibrate the prediction results and validate of the machine learning model. MOFs with competitively high C3H8/C3H6 separation potential and regenerability were identified, and a comparison with MOFs reported in the literature was made, indicating that the proposed machine learning-assisted high-throughput screening approach is efficient and effective. Furthermore, structure–property correlation analysis was conducted.

Inverse design of metal–organic frameworks for C2H4/C2H6 separation

Efficient separation of C2H4/C2H6 mixtures is of paramount importance in the petrochemical industry. Nanoporous materials, especially metal-organic frameworks (MOFs), may serve the purpose owing to their tailorable structures and pore geometries. In this work, we propose a computational framework for high-throughput screening and inverse design of high-performance MOFs for adsorption and membrane processes. High-throughput screening of the computational-ready, experimental (CoRE 2019) MOF database leads to materials with exceptionally high ethane-selective adsorption selectivity (LUDLAZ: 7.68) and ethene-selective membrane selectivity (EBINUA02: 2167.3). Moreover, the inverse design enables the exploration of broader chemical space and identification of MOF structures with even higher membrane selectivity and permeability. In addition, a relative membrane performance score (rMPS) has been formulated to evaluate the overall membrane performance relative to the Robeson boundary. The computational framework offers guidelines for the design of MOFs and is generically applicable to materials discovery for gas storage and separation.

Band-gap assessment from X-ray powder diffraction using artificial intelligence

X-ray diffraction is a phenomenon that stems from the interaction of the electron density of a crystalline material and the electric field of the X-ray waves. The product of this interaction, the diffraction pattern, provides a picture of the reciprocal space of the atomic distribution in terms of intensities of certain scattering wavevectors. In this manner, a correlation between those intensities seen in a diffraction pattern and the electronic properties of a material is suggested. This correlation, if it exists, may not be directly proposed using analytical expressions. This article shows for the first time the feasibility of assessing the band gap of metal–organic frameworks (MOFs) and organic and inorganic materials from their X-ray powder diffraction pattern. The band gaps were assessed with convolutional neural networks (CNNs). These CNNs were developed using simulated X-ray powder diffraction patterns and the band gaps calculated with density functional theory. The diffraction patterns were simulated with different crystal sizes, from 10 nm to the macrocrystalline size. In addition, the reported band gaps of MOFs and organic compounds in the Quantum MOF Database and the Organic Materials Database data sets were used, which were calculated with the PBE functional. Furthermore, the band gaps calculated by Kim et al. [Sci. Data (2020), 7, 387] for inorganic compounds with the HSE functional were used. The developed CNNs were tested with simulated diffraction patterns of compounds different from those used to train the CNNs, as well as with experimentally recorded diffraction patterns. The developed CNNs allowed the assessment of the band gap of the compounds with a root-mean-square error as low as 0.492 eV after training with over 64 000 diffraction patterns.

Mapping the Porous and Chemical Structure-Function Relationships of Trace CH3I Capture by Metal-Organic Frameworks using Machine Learning.

Large-scale computational screening has become an indispensable tool for functional materials discovery. It, however, remains a challenge to adequately interrogate the large amount of data generated by a screening study. Here, we computationally screened 1087 metal-organic frameworks (MOFs), from the CoRE MOF 2014 database, for capturing trace amounts (300 ppmv) of methyl iodide (CH3I); as a primary representative of organic iodides, CH3129I is one of the most difficult radioactive contaminants to separate. Furthermore, we demonstrate a simple and general approach for mapping and interrogating the high-dimensional structure-function data obtained by high-throughput screening; this involves learning two-dimensional embeddings of the high-dimensional data by applying unsupervised learning to encoded structural and chemical features of MOFs. The resulting various porous and chemical structure-function maps are human-interpretable, revealing not only top-performing MOFs but also complex structure-function correlations that are hidden when inspecting individual MOF features. These maps also alleviate the need of laborious visual inspection of a large number of MOFs by clustering similar MOFs, per the encoding features, into defined regions on the map. We also show that these structure-function maps are amenable to supervised classification of the performances of MOFs for trace CH3I capture. We further show that the machine-learning models trained on the 1087 CoRE MOFs can be used to predict an unseen set of 250 MOFs randomly selected from a different MOF database, achieving high prediction accuracies.

Demonstration of High-Throughput Building Block and Composition Analysis of Metal-Organic Frameworks.

Metal-organic frameworks (MOFs) are composed of inorganic metal-containing nodes and organic linker groups and are promising porous materials for a wide range of applications. More than 90 000 different MOFs have been synthesized with different inorganic nodes, organic linkers, and node-linker connectivity patterns. While databases have been created to catalog this enormous number of structures, they generally do not provide functionality to easily search, sort, and understand MOFs based on composition and building blocks. Because structure-property relationships are critical to identify, here we outline our new program MOFseek and demonstrate that it can perform high-throughput structure and composition analyses of MOF structures. This program enables the fast analysis of tens of thousands of MOFs in databases based on the local chemical environment. We demonstrate the unique capabilities of MOFseek by analyzing the CoRE MOF database of structures.

Molecular-fingerprint machine-learning-assisted design and prediction for high-performance MOFs for capture of NMHCs from air

The capture of trace amounts of non-methane hydrocarbons (NMHCs) from air due to the toxicity of volatile organic compounds is a significant challenge. A total of 31399 hydrophobic metal–organic frameworks (MOFs) were first screened from 137953 hypothetical MOFs using high-throughput computational screening (HTCS), and their performance indices (adsorption capacity and selectivity) for the adsorption of NMHCs (C 3 –C 6 ) were obtained by molecular simulations. The discovery of a “second peak” near twice the kinetic diameter of the corresponding NMHC provided more choices for excellent MOFs that adsorb NMHCs. Four machine learning (ML) classification and regression algorithms predicted the performance of MOFs, and the relative importance values of the six descriptors were determined. The combination of the Random Forests algorithm and Molecular ACCess Systems molecular fingerprint (MF) had an excellent predictive ability for MOFs. According to the performance, the fingerprint commonalities of the 100 top-performing MOFs were counted, and the excellent bits (EBs) that could promote the performance were defined. Finally, new substructures containing all of the EBs were designed for each NMHC to build a new MOF database. This work combined the HTCS, ML, and MF to provide a detailed insight into the design of efficient MOFs for adsorbing NMHCs.

Hydrogen storage metal-organic framework classification models based on crystal graph convolutional neural networks

Metal-organic frameworks (MOFs) have been considered as promising physical adsorbents for hydrogen storage due to their high porosity and structural tunability. We selected 7643 real MOFs from the computation-ready MOF 2019 database to screen high-performance materials for hydrogen storage based on the grand canonical Monte Carlo (GCMC) simulations. Based on the obtained data set, we proposed a deep learning classification model powered by the crystal graph convolutional neural networks (CGCNN) for the discovery of the optimal hydrogen storage MOFs. It is demonstrated that our classification model based on CGCNN algorithms exhibits a high prediction accuracy with the area under the ROC-plot curve (AUC) of 0.9208 for hydrogen storage performance of volumetric deliverable capacity (VDC). Compared with the classification models based on other machine learning algorithms such as random forest, our CGCNN model demonstrates the advantages of fast prediction with no feature extraction and little accuracy loss. When the trained CGCNN model was used to predict the classification of the unfamiliar samples (the randomly selected 1000 MOFs from the 137,953 hypothetical MOF database), we also obtain high accuracy with AUC > 0.81, indicating that this model exhibits reliable transferability for other types of MOFs. Meanwhile, we also elucidated the relationship between structure and performance of MOFs for hydrogen storage using the decision tree algorithms and quantitative structure–property analysis. Furthermore, the hydrogen adsorption performance and mechanism of top-performance MOFs were analyzed by adsorption isotherms, radial distribution functions, and mass center density distribution of equilibrium configurations. All those insights from atomic simulations and machine learnings can accelerate the discovery of new nanoporous materials not only for gas adsorption in MOFs but also for gas separation in other types of porous materials.

Graph neural network predictions of metal organic framework CO[formula omitted] adsorption properties

The increasing CO2 level is a critical concern and suitable materials are needed to capture such gases from the environment. While experimental and conventional computational methods are useful in finding such materials, they are usually slow and there is a need to expedite such processes. We use Atomistic Line Graph Neural Network (ALIGNN) method to predict CO2 adsorption in metal organic frameworks (MOF), which are known for their high functional tunability. We train ALIGNN models for hypothetical MOF (hMOF) database with 137953 MOFs with grand canonical Monte Carlo (GCMC) based CO2 adsorption isotherms. We develop high accuracy and fast models for pre-screening applications. We apply the trained model on CoREMOF database and computationally rank them for experimental synthesis. In addition to the CO2 adsorption isotherm, we also train models for electronic bandgaps, surface area, void fraction, lowest cavity diameter, and pore limiting diameter, and illustrate the strength and limitation of such graph neural network models. For a few candidate MOFs we carry out GCMC calculations to evaluate the deep-learning (DL) predictions.

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning.

Despite rapid progress in the field of metal–organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly predicting the synthesis conditions of a MOF based on its crystal structure. Our approach is based on: i) establishing the first MOF synthesis database via automatic extraction of synthesis parameters from the literature, ii) training and optimizing ML models by employing the MOF database, and iii) predicting the synthesis conditions for new MOF structures. The ML models, even at an initial stage, exhibit a good prediction performance, outperforming human expert predictions, obtained through a synthesis survey. The automated synthesis prediction is available via a web‐tool on https://mof‐synthesis.aimat.science.

Incorporating Flexibility Effects into Metal-Organic Framework Adsorption Simulations Using Different Models.

High-throughput calculations based on molecular simulations to predict the adsorption of molecules inside metal-organic frameworks (MOFs) have become a useful complement to experimental efforts to identify promising adsorbents for chemical separations and storage. For computational convenience, all existing efforts of this kind have relied on simulations in which the MOF is approximated as rigid. In this paper, we use extensive adsorption-relaxation simulations that fully include MOF flexibility effects to explore the validity of the rigid framework approximation. We also examine the accuracy of several approximate methods to incorporate framework flexibility that are more computationally efficient than adsorption-relaxation calculations. We first benchmark various models of MOF flexibility for four MOFs with well-established CO2 experimental consensus isotherms. We then consider a range of adsorption properties, including Henry's constants, nondilute loadings, and adsorption selectivity, for seven adsorbates in 15 MOFs randomly selected from the CoRE MOF database. Our results indicate that in many MOFs adsorption-relaxation simulations are necessary to make quantitative predictions of adsorption, particularly for adsorption at dilute concentrations, although more standard calculations based on rigid structures can provide useful information. Finally, we investigate whether a correlation exists between the elastic properties of empty MOFs and the importance of including framework flexibility in making accurate predictions of molecular adsorption. Our results did not identify a simple correlation of this type.