Hypothetical Metal-organic Frameworks Research Articles

Machine learning insights into predicting biogas separation in metal-organic frameworks

Breakthroughs in efficient use of biogas fuel depend on successful separation of carbon dioxide/methane streams and identification of appropriate separation materials. In this work, machine learning models are trained to predict biogas separation properties of metal-organic frameworks (MOFs). Training data are obtained using grand canonical Monte Carlo simulations of experimental MOFs which have been carefully curated to ensure data quality and structural viability. The models show excellent performance in predicting gas uptake and classifying MOFs according to the trade-off between gas uptake and selectivity, with R2 values consistently above 0.9 for the validation set. We make prospective predictions on an independent external set of hypothetical MOFs, and examine these predictions in comparison to the results of grand canonical Monte Carlo calculations. The best-performing trained models correctly filter out over 90% of low-performing unseen MOFs, illustrating their applicability to other MOF datasets.

Theory-based design principles for unprecedentedly high two-level CO2 utilization of CO2-derived metal-organic frameworks

In this study, we theoretically developed a novel strategy for “two-level” CO2 utilization: the first level is “CO2 content” by directly converting CO2 into metal-organic frameworks (MOFs), and the second level is “CO2 capacity” by storing CO2 gas into their cavity. Despite its conceptual simplicity, experimental realization is rare due to technical difficulties and a lack of design principles for simultaneously maximizing CO2 content and CO2 capacity, leaving many unexplored possibilities. In this study, we designed over 1,500 hypothetical MOFs across 15 types of metal nodes combined with 14 types of newly designed CO2-derived ligands in over 40 topologies. We found that [-CO2NHNHCO2-] ligand assembled with [V2(CO2)4] node on the zeolite-like usf topology can have a large CO2 content of 50 wt%, which exceeds 31.6 wt% for the synthesized CO2-derived MOF. Grand Canonical Monte Carlo Simulation showed that the well-alignment of the paddlewheel in usf topology allows CO2 to be fully exposed to the cavity space, achieving high CO2 adsorption capacity. In addition, the fluorination of the ligand of MOFs significantly increases CO2 capacity and reaches unprecedented high CO2 utilization of up to 62 wt%. The design principles and database from this work provide opportunities and challenges for experimentalists in synthesizing new MOFs with breakthrough CO2 utilization.

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.

A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture

Metal-organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six AI-generated MOFs with CO2 capacities greater than 2m mol g−1, i.e., higher than 96.9% of structures in the hypothetical MOF dataset.

Accelerating In Silico Discovery of Metal-Organic Frameworks for Ethane/Ethylene and Propane/Propylene Separation: A Synergistic Approach Integrating Molecular Simulation, Machine Learning, and Active Learning.

Cryogenic distillation, a currently employed method for C2H4/C2H6 and C3H6/C3H8 mixture separation, is energy-intensive, prompting the research toward alternative technologies, including adsorbent-based separation. In this work, we combine machine learning (ML) technique with high-throughput screening to screen ∼23,000 hypothetical metal-organic frameworks (MOFs) for paraffin (C2H6 and C3H8) selective adsorbent separation. First, structure-based prescreening was employed to remove MOFs with undesired geometric properties. Further, a random forest model built upon the multicomponent grand canonical Monte Carlo (m-GCMC) simulation data of training set MOFs was found to be the most successful in learning the relationship between MOF features and olefin/paraffin mixture separation. Using this technique, the separation performance of the remaining (test set) MOFs was predicted, and the top-performing MOFs were identified. We also employed active learning (AL) to evaluate its effectiveness in improving the prediction of olefin/paraffin selectivity. AL was discovered to be ∼29 times more efficient than the best-supervised ML model, as it was able to identify the top materials in limited training data and at a fraction of computational cost and time as compared to ML techniques. Among the top selected materials, framework chemistry was found to be the most important parameter. Nickel and copper (as a metal node) in a tfzd and hms topological arrangement respectively, were discovered to be a prevalent attribute in high-performing MOFs, further demonstrating the prominent significance of framework chemistry. Additionally, the top MOFs discovered were studied in detail and further compared to the previously reported MOFs. These MOFs show the highest selectivity for C2H4/C2H6 and C3H6/C3H8 mixture separation, as reported until date. The hierarchical strategy devised in this study will facilitate the quick screening of MOFs across multiple databases toward industrially significant separation processes by leveraging molecular simulations and AL.

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.

Exploring Hydrogen Storage Capacity in Metal-Organic Frameworks: A Bayesian Optimization Approach.

Metal-organic Frameworks (MOFs) can be employed for gas storage, capture, and sensing. Finding the MOF with the best adsorption property from a big database is usual for adsorption calculations. In high-throughput computational research, the expense of computing thermodynamic quantities limits the finding of MOFs for separations and storage.In this work, we demonstrate the usefulness of Bayesian optimization (BO) for estimating the H2 uptake capability of MOFs by using an existing dataset containing 98 000 real as well as hypothetical MOFs. It is demonstrated that in order to recover the best candidate MOFs, less than 0.027% of the database needs to be screened using the BO method. This allows future adsorption experiments on a small sample of MOFs to be undertaken with minimal experimental effort by effectively screening MOF databases. In addition, the presented BO can provide comprehensible material design insights, and the framework will be transferable to the setting of other target properties. We suggest using particle swarm optimisation (PSO), a swarm intelligence technique in artificial intelligence, to estimate MOFs' H2 uptake potential to achieve results comparable to BO. In addition, we have implemented the novel evolutionary-PSO (EPSO) method for comparison research and found some extremely interesting outcomes.

Host–guest interactions in framework materials: Insight from modeling

The performance of metal–organic and covalent organic framework materials in sought-after applications—capture, storage, and delivery of gases and molecules, and separation of their mixtures—heavily depends on the host–guest interactions established inside the pores of these materials. Computational modeling provides information about the structures of these host–guest complexes and the strength and nature of the interactions present at a level of detail and precision that is often unobtainable from experiment. In this Review, we summarize the key simulation techniques spanning from molecular dynamics and Monte Carlo methods to correlate ab initio approaches and energy, density, and wavefunction partitioning schemes. We provide illustrative literature examples of their uses in analyzing and designing organic framework hosts. We also describe modern approaches to the high-throughput screening of thousands of existing and hypothetical metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) and emerging machine learning techniques for predicting their properties and performances. Finally, we discuss the key methodological challenges on the path toward computation-driven design and reliable prediction of high-performing MOF and COF adsorbents and catalysts and suggest possible solutions and future directions in this exciting field of computational materials science.

Implementing and understanding the unsupervised transfer learning in metal organic framework toward methane adsorption from hypothetical to experimental data

In recent years, many research groups have built a broad array of metal organic framework (MOF) materials databases with computational high-throughput screening. How to utilize the knowledge extracted from these computational datasets to study experimentally synthesized MOFs is an attractive issue. Transfer learning (TL) can solve this problem because of its superior performance in compensating for missing information in data. In this work, we adopt an unsupervised TL framework to predict the methane adsorption capability of synthesized MOFs based on a hypothetical MOF database. Unlike many reported supervised TL models, all reference values on the target synthesized MOF database are absent during TL, which is more challenging and more realistic for new material discovery. The best accuracy of TL from hMOF to CoRE MOF is 70%, and that from tobacco MOF to CoRE MOF achieves 86%. The impact of input features and loss functions on TL is discussed carefully. It can be observed that both geometric descriptors and engineering descriptors play an important role in TL, especially the set of the sure independence screening and sparsifying operator (SISSO) refined engineering descriptors usually performs better than the primary features. Besides, the feature importance analysis helps understand the biases between hypothetical and experimental databases and gives chemical insights into the application of TL. This study provides a practical prospect of machine learning in experimental data processing.

Integrating stability metrics with high-throughput computational screening of metal–organic frameworks for CO2 capture

Metal–organic frameworks (MOFs) have been considered a unique class of hybrid materials for a wide variety of potential applications. With the existence of almost infinite MOFs, high-throughput computational screening (HTCS) is a robust technique to accelerate the search for promising MOFs. However, conventional HTCS studies reported in the literature neglect the stability of MOFs, which must be considered for practical applications. Here we integrate four stability metrics (thermodynamic, mechanical, thermal, and activation) with HTCS to identify top-performing, synthesizable, and stable hypothetical MOFs for CO2 capture. The thermodynamic and mechanical stabilities are evaluated through molecular dynamics simulations, while the activation and thermal stabilities are predicted using machine learning models. Finally, we identify top-performing hypothetical MOFs satisfying all these stability metrics. This study underlines the central importance of integrating stability metrics when screening MOFs for applications.

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.

Correction to “Screening of Hypothetical Metal–Organic Frameworks for Xylene Isomers and Ethylbenzene Separation”

ADVERTISEMENT RETURN TO ISSUEPREVAddition/CorrectionNEXTORIGINAL ARTICLEThis notice is a correctionCorrection to “Screening of Hypothetical Metal–Organic Frameworks for Xylene Isomers and Ethylbenzene Separation”Prosun HalderProsun HalderMore by Prosun Halderhttps://orcid.org/0000-0002-3988-2712 and Jayant K. Singh*Jayant K. SinghMore by Jayant K. Singhhttps://orcid.org/0000-0001-8056-2115Cite this: Energy Fuels 2023, 37, 6, 4726–4728Publication Date (Web):February 23, 2023Publication History Received15 February 2023Published online23 February 2023Published inissue 16 March 2023https://doi.org/10.1021/acs.energyfuels.3c00500Copyright © 2023 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views119Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (2 MB) Get e-Alerts Get e-Alerts

MOFormer: Self-Supervised Transformer Model for Metal-Organic Framework Property Prediction.

Metal-organic frameworks (MOFs) are materials with a high degree of porosity that can be used for many applications. However, the chemical space of MOFs is enormous due to the large variety of possible combinations of building blocks and topology. Discovering the optimal MOFs for specific applications requires an efficient and accurate search over countless potential candidates. Previous high-throughput screening methods using computational simulations like DFT can be time-consuming. Such methods also require the 3D atomic structures of MOFs, which adds one extra step when evaluating hypothetical MOFs. In this work, we propose a structure-agnostic deep learning method based on the Transformer model, named as MOFormer, for property predictions of MOFs. MOFormer takes a text string representation of MOF (MOFid) as input, thus circumventing the need of obtaining the 3D structure of a hypothetical MOF and accelerating the screening process. By comparing to other descriptors such as Stoichiometric-120 and revised autocorrelations, we demonstrate that MOFormer can achieve state-of-the-art structure-agnostic prediction accuracy on all benchmarks. Furthermore, we introduce a self-supervised learning framework that pretrains the MOFormer via maximizing the cross-correlation between its structure-agnostic representations and structure-based representations of the crystal graph convolutional neural network (CGCNN) on >400k publicly available MOF data. Benchmarks show that pretraining improves the prediction accuracy of both models on various downstream prediction tasks. Furthermore, we revealed that MOFormer can be more data-efficient on quantum-chemical property prediction than structure-based CGCNN when training data is limited. Overall, MOFormer provides a novel perspective on efficient MOF property prediction using deep learning.

High-throughput screening of hypothetical metal-organic frameworks for thermal conductivity

Thermal energy management in metal-organic frameworks (MOFs) is an important, yet often neglected, challenge for many adsorption-based applications such as gas storage and separations. Despite its importance, there is insufficient understanding of the structure-property relationships governing thermal transport in MOFs. To provide a data-driven perspective into these relationships, here we perform large-scale computational screening of thermal conductivity k in MOFs, leveraging classical molecular dynamics simulations and 10,194 hypothetical MOFs created using the ToBaCCo 3.0 code. We found that high thermal conductivity in MOFs is favored by high densities (> 1.0 g cm−3), small pores (< 10 Å), and four-connected metal nodes. We also found that 36 MOFs exhibit ultra-low thermal conductivity (< 0.02 W m−1 K−1), which is primarily due to having extremely large pores (~65 Å). Furthermore, we discovered six hypothetical MOFs with very high thermal conductivity (> 10 W m−1 K−1), the structures of which we describe in additional detail.

Design and Screening of Metal-Organic Frameworks for Ethane/Ethylene Separation.

Separation of ethane and ethylene is considered to be industrially important for various chemical processes, but their similarities make the process expensive. In this study, we integrated computational screening with machine learning to find optimal metal-organic frameworks (MOFs) with high ethane/ethylene selectivity. Using our algorithm, a hypothetical MOF structure with an ideal adsorption solution theory selectivity of 3.6 at 298 K and 1 bar was discovered. Furthermore, structural analysis was implemented, and the full adsorption isotherm of some of the top structures was obtained.

Screening of Hypothetical Metal–Organic Frameworks for Xylene Isomers and Ethylbenzene Separation

A database of hypothetical metal–organic frameworks (hMOFs) was investigated in search of top-performing adsorbents for the separation process of xylene isomers and ethylbenzene mixture. The structural properties of individual hMOF are calculated to understand the correlation between the properties with adsorption capacity or selectivity. The high-throughput staged screening of the hMOF database is performed and screened. The simulations of hMOF with a gas mixture were conducted at 373 K and 30 kPa using a multicomponent grand canonical Monte Carlo method to obtain the para-xylene selectivity. The top five hMOFs showed significantly better performance of all and compared to previously discovered materials. The top hMOFs were further simulated at different pressures to obtain the adsorption isotherms. Further, the energetic contribution of organic linkers and inorganic metal nodes toward competitive adsorption was studied to further understand the enhanced interaction between hMOF and para-xylene.

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.

The HEALED SBU Library of Chemically Realistic Building Blocks for Construction of Hypothetical Metal-Organic Frameworks.

Advancements in hypothetical metal-organic framework (hMOF) databases and construction tools have resulted in a rapidly expanding chemical design space for nanoporous materials. The bulk of these hypothetical structures are constructed using structural building units (SBUs) derived from experimental MOF structures, often collected from the CoRE-MOF database. Recent investigations into the state of these deposited experimental structures' chemical accuracy identified an array of common structural errors, including omitted protons, missing counterions, and disordered structures. These structural errors propagate into the SBUs mined from experimental MOFs, culminating in inaccurate hMOF structures possessing net charges or missing atoms which were not accounted for previously. This work demonstrates how manual investigation was applied to diagnose structural errors in SBUs obtained from several popular hMOF construction tools and databases. An analysis of the prevailing errors discovered during the examination process is provided along with representative cases to aid with error detection in future studies involving SBU extraction and hMOF construction. A novel repair protocol was established and employed to generate a library of SBUs that are hand-examined and labeled with enhanced detail (HEALED). This repaired library of SBUs contains 952 inorganic SBUs and 568 organic SBUs ideally suited for the generation of hypothetical frameworks that are chemically accurate and properly charge labeled. Additionally, case studies following the effects of SBU errors on electrostatic potential-fitted charges and GCMC-simulated gas adsorption predictions are presented to highlight the significance of using chemically accurate hMOF structures exclusively in all screening efforts going forward.

Combining Machine Learning and Molecular Simulations to Unlock Gas Separation Potentials of MOF Membranes and MOF/Polymer MMMs.

Due to the enormous increase in the number of metal-organic frameworks (MOFs), combining molecular simulations with machine learning (ML) would be a very useful approach for the accurate and rapid assessment of the separation performances of thousands of materials. In this work, we combined these two powerful approaches, molecular simulations and ML, to evaluate MOF membranes and MOF/polymer mixed matrix membranes (MMMs) for six different gas separations: He/H2, He/N2, He/CH4, H2/N2, H2/CH4, and N2/CH4. Single-component gas uptakes and diffusivities were computed by grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations, respectively, and these simulation results were used to assess gas permeabilities and selectivities of MOF membranes. Physical, chemical, and energetic features of MOFs were used as descriptors, and eight different ML models were developed to predict gas adsorption and diffusion properties of MOFs. Gas permeabilities and membrane selectivities of 5249 MOFs and 31,494 MOF/polymer MMMs were predicted using these ML models. To examine the transferability of the ML models, we also focused on computer-generated, hypothetical MOFs (hMOFs) and predicted the gas permeability and selectivity of 1000 hMOF/polymer MMMs. The ML models that we developed accurately predict the uptake and diffusion properties of He, H2, N2, and CH4 gases in MOFs and will significantly accelerate the assessment of separation performances of MOF membranes and MOF/polymer MMMs. These models will also be useful to direct the extensive experimental efforts and computationally demanding molecular simulations to the fabrication and analysis of membrane materials offering high performance for a target gas separation.