Hypothetical Metal-organic Frameworks Research Articles

MOFSynth: A Computational Tool toward Synthetic Likelihood Predictions of MOFs.

In the past decade, high-throughput computational studies of materials have increased significantly mainly due to advances in computer capabilities and have attracted a great deal of interest. In the field of metal-organic frameworks (MOFs), over a million hypothetical MOFs have been designed in silico, yet only a small fraction of these have been synthesized. For validating the computational-hypothetical results and accelerating the progress in the field, there is a pressing need for distinguishing MOFs that are more likely to be synthesized for real-life applications. This study presents a comprehensive investigation into the synthesizability likelihood of MOFs, utilizing a novel computational approach based on the disparities in energy and geometry between the linker conformation within the MOF structure and its isolated, free-gas state since both of these have been proven to be critical factors influencing MOF synthesis. Our user-friendly tool streamlines synthesizability evaluation, requiring minimal expertise in computational chemistry. By deconstructing over 40,000 MOFs from databases, including QMOF, CoRE MOF, and ToBaCCo, we analyze key parameters defining the linker strain within the MOF unit cell. Our results indicate that QMOF and CoRE MOF contain more promising candidates for synthesis, while ToBaCCo exhibits a relatively poor synthesizability likelihood due to unoptimized materials. Through extensive analysis, we identify optimal linker candidates for highly synthesizable MOFs. Consistent trends in energy distribution across databases that are confirmed by high Pearson and Spearman coefficients suggest the potential for omitting optimization calculations, significantly reducing computational costs. This study underscores the importance of linker deformation and energy disparities and enhances our understanding of synthetic accessibility in MOF research, offering valuable insights for future advancements in the field.

Understanding CO adsorption in MOFs combining atomic simulations and machine learning

This study introduces a computational method integrating molecular simulations and machine learning (ML) to assess the CO adsorption capacities of synthesized and hypothetical metal–organic frameworks (MOFs) at various pressures. After extracting structural, chemical, and energy-based features of the synthesized and hypothetical MOFs (hMOFs), we conducted molecular simulations to compute CO adsorption in synthesized MOFs and used these simulation results to train ML models for predicting CO adsorption in hMOFs. Results showed that CO uptakes of synthesized MOFs and hMOFs are between 0.02–2.28 mol/kg and 0.45–3.06 mol/kg, respectively, at 1 bar, 298 K. At low pressures (0.1 and 1 bar), Henry’s constant of CO is the most dominant feature, whereas structural properties such as surface area and porosity are more influential for determining the CO uptakes of MOFs at high pressure (10 bar). Structural and chemical analyses revealed that MOFs with narrow pores (4.4–7.3 Å), aromatic ring-containing linkers and carboxylic acid groups, along with metal nodes such as Co, Zn, Ni achieve high CO uptakes at 1 bar. Our approach evaluated the CO uptakes of ~ 100,000 MOFs, the most extensive and diverse set studied for CO capture thus far, as a robust alternative to computationally demanding molecular simulations and iterative experiments.

Accelerating Discovery of Mechanically Stable Metal-Organic Frameworks for Vinylidene Fluoride Storage by Active Learning.

Metal-organic frameworks (MOFs) are versatile nanoporous materials for a wide variety of important applications. Recently, a handful of MOFs have been explored for the storage of toxic fluorinated gases (Keasler et al. Science, 2023, 381, 1455), yet the potential of a great number of MOFs for such an environmentally sustainable application has not been thoroughly investigated. In this work, we apply active learning (AL) to accelerate the discovery of hypothetical MOFs (hMOFs) that can efficiently store a specific fluorinated gas, namely, vinylidene fluoride (VDF). First, a force field was developed for VDF and utilized to predict the working capacities (ΔN) of VDF in an initial data set of 4502 MOFs from the computation-ready experimental MOF (CoRE-MOF) database that successfully underwent featurization and grand-canonical Monte Carlo simulations. Next, the initial data set was diversified by Greedy sampling in an unexplored sample space of 119,387 hMOFs from the ab initio REPEAT charge MOF (ARC-MOF) database. A budget of 10,000 samples (i.e., <10% of total ARC-MOFs) was selected to train a random forest model. Then, ΔN in the unlabeled ARC-MOFs were predicted and top-performing ones were validated by simulations. Integrating with the stability requirement, mechanically stable ARC-MOFs were finally identified, along with high ΔN. Furthermore, by Pareto-Frontier analysis, we revealed that long linear linkers can enhance ΔN, while bulkier multiphenyl linkers or interpenetrated frameworks improve mechanical strength. From this work, we efficiently discover top-performing MOFs for VDF storage by AL and also demonstrate the importance of integrating stability to identify stable promising MOFs for a practical application.

MOFs with the Stability for Practical Gas Adsorption Applications Require New Design Rules.

Metal-organic frameworks (MOFs) have been widely studied for their ability to capture and store greenhouse gases. However, most computational discovery efforts study hypothetical MOFs without consideration of their stability, limiting the practical application of novel materials. We overcome this limitation by screening hypothetical ultrastable MOFs that have predicted high thermal and activation stability, as judged by machine learning (ML) models trained on experimental measures of stability. We enhance this set by computing the bulk modulus as a measure of mechanical stability and filter 1102 mechanically robust hypothetical MOFs from a database of ultrastable MOFs (USMOF DB). Grand Canonical Monte Carlo simulations are then employed to predict the gas adsorption properties of these hypothetical MOFs, alongside a database of experimental MOFs. We identify privileged building blocks that lead MOFs in USMOF DB to show exceptional working capacities compared to the experimental MOFs. We interpret these differences by training ML models on CO2 and CH4 adsorption in these databases, showing how poor model transferability between data sets indicates that novel design rules can be derived from USMOF DB that would not have been gathered through assessment of structurally characterized MOFs. We identify geometric features and node chemistry that will enable the rational design of MOFs with enhanced gas adsorption properties in synthetically realizable MOFs.

Machine Learning Predictions of Methane Storage in MOFs: Diverse Materials, Multiple Operating Conditions, and Reverse Models.

A machine learning (ML) model is developed for predicting useable methane (CH4) capacities in metal-organic frameworks (MOFs). The model applies to a wide variety of MOFs, including those with and without open metal sites, and predicts capacities for multiple pressure swing conditions. Despite its wider applicability, the model requires only 5 measurable structural features as input, yet achieves accuracies that surpass less-general models. Application of the model to a database of more than a million hypothetical MOFs identified several hundred whose capacities surpass that of the benchmark MOF, UMCM-152. Guided by the computational predictions, one of the promising candidates, UMCM-153, was synthesized and demonstrated to achieve superior volumetric capacity for CH4. Feature importance analyses reveal that pore volume and gravimetric surface area are the most important features for predicting CH4 capacity in MOFs. Finally, a reverse ML model is demonstrated. This model predicts the set of elementary MOF structural properties needed to achieve a desired CH4 capacity for a prescribed operating condition.

Finding high-performance MOFs for effective SF6/N2 separation through high-throughput computational screening and machine learning

Abstract Given the rapidly expanding pool of synthesized and hypothetical metal–organic frameworks (MOFs), testing every single material for SF6/N2 separation by iterative experimental methods or computationally demanding molecular simulations is not practical. In this study, we integrated high-throughput computational screening and machine learning (ML) approaches to evaluate SF6/N2 mixture adsorption and separation performances of over 25 000 different types of synthesized and hypothetical MOFs (hypoMOFs), representing the largest set of structures studied for SF6/N2 separation to date. SF6/N2 mixture adsorption data that we produced for synthesized MOFs using molecular simulations were utilized to develop ML models to accurately and quickly predict SF6 and N2 uptakes, SF6/N2 selectivities, SF6 working capacities, adsorbent performance scores, and regenerabilities of both synthesized and hypoMOFs. Results showed the MOF space that we studied exhibits very high SF6/N2 selectivities in the range of 1.8–4204 at 1 bar in addition to high SF6 working capacities between 0.04–5.68 mol kg−1 at an adsorption pressure of 1 bar and desorption pressure of 0.1 bar at room temperature. The top-performing MOF adsorbents for SF6/N2 mixture separation were identified to have Zn, Cu, Ni metals; terphenyl, pyridine, naphthalene linkers; and medium pore sizes. Our comprehensive computational approach offers a highly efficient alternative to brute-force computer simulations by enabling the rapid assessment of the MOF adsorbents for SF6/N2 separation and provides molecular insights into the key structural features of the most promising adsorbents.

Uncovering the Relationship between Metal Elements and Mechanical Stability for Metal-Organic Frameworks.

Assessing the mechanical robustness of metal-organic frameworks (MOFs) is crucial to enhance their applicability in various fields. Although considerable research has been conducted on the relationship between the mechanical properties of MOFs and their structural features (such as pore size, surface area, and topology), the insufficient exploration of metal elements has prevented researchers from fully understanding their mechanical behavior. To plug this knowledge gap, we constructed a database of mechanical properties for 20,342 MOFs included in the QMOF database using molecular simulations to investigate the impact of metal elements on mechanical stability. Through Shapley additive explanations (SHAP) analysis, we found that Co and Ln could enhance the structural stability of MOFs. We validated these findings using newly generated hypothetical MOFs. Notably, we adopted an interpretable machine learning technique to analyze the contribution of remarkably diverse metal elements in the 20,342 MOFs to the mechanical properties of each MOF. We anticipate that this research will serve as a valuable tool for future studies on identifying mechanically robust MOFs suitable for various industrial applications.

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