Large-scale computational assembly of ionic liquid/MOF composites: synergistic effect in the wire-tube conformation for efficient CO<sub>2</sub>/CH<sub>4</sub> separation

Lan, Youshi; Yan, Tongan; Tong, Minman; Zhong, Chongli

doi:10.1039/c9ta01752f

Cited by 54 publications

(59 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Expanding upon these previous reports, this study applies ML to explore a large database of 918,734 known and proposed MOFs. The database was assembled from a diverse collection of publicly available MOF repositories, 1 , 29 , 31 , 33 , 34 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 81 , 82 and allows for a wide-ranging and consistent assessment of H 2 uptake in MOFs.…”

Section: Introductionmentioning

confidence: 99%

“…A database of crystal structures for 918,734 MOFs was created by combining 19 existing databases. 1 , 29 , 31 , 33 , 34 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 81 , 82 Table 2 summarizes the source databases and the number of MOFs contained in each. Out of these 19 databases, only the UM, 31 CSD, 29 , 30 and CoRE 33 , 34 databases contain data on MOFs that have been previously synthesized.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting hydrogen storage in MOFs via machine learning

Ahmed

Siegel

2021

Patterns

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting hydrogen storage in MOFs via machine learning

Ahmed

Siegel

2021

Patterns

View full text Add to dashboard Cite

show abstract

“…With the ratio of CO 2 /CH 4 in mixture at 50:50, the adsorption on MOFs was simulated with GCMC method at 298 K and 1 bar using the same method as in Xe/Kr. Note that the best separation material in the G-MOFs database is Zn 2 O 8 -BTC_B-irmof7_A_No16, 26 corresponding to the selectivity of CO 2 /CH 4 at 50.5 and uptake of CO 2 at 4.0 mmol/g, which is lower than SAJFEO with the CO 2 /CH 4 selectivity at 210.33 and uptake of CO 2 at 6.31 mmol/g. 55 Through the data analysis as listed in Table S3 , we found that the calculated adsorption properties in G-MOFs datasets are obviously imbalanced, especially for the adsorption selectivity of CO 2 /CH 4 .…”

Section: Results and Discussionmentioning

confidence: 98%

“… 26 To date, 162 thoroughly different MOF structures have been synthesized experimentally in G-MOFs. 26 …”

Section: Computational Detailsmentioning

confidence: 99%

See 1 more Smart Citation

XGBoost: An Optimal Machine Learning Model with Just Structural Features to Discover MOF Adsorbents of Xe/Kr

Heng

Jiang²,

Yan

et al. 2021

ACS Omega

View full text Add to dashboard Cite

The inert gases Xe and Kr mainly exist in the used nuclear fuel (UNF) with the Xe/Kr ratio of 20:80, which it is difficult to separate. In this work, based on the G-MOFs database, high-throughput computational screening for metal–organic frameworks (MOFs) with high Xe/Kr adsorption selectivity was performed by combining grand canonical Monte Carlo (GCMC) simulations and machine learning (ML) technique for the first time. From the comparison of eight classical ML models, it is found that the XGBoost model with seven structural descriptors has superior accuracy in predicting the adsorption and separation performance of MOFs to Xe/Kr. Compared with energetic or electronic descriptors, structural descriptors are easier to obtain. Note that the determination coefficients R 2 of the generalized model for the Xe adsorption and Xe/Kr selectivity are very close to 1, at 0.951 and 0.973, respectively. In addition, 888 and 896 MOFs have been successfully predicted by the XGBoost model among the top 1000 MOFs in adsorption capacity and selectivity by GCMC simulation, respectively. According to the feature engineering of the XGBoost model, it is shown that the density (ρ), porosity (ϕ), pore volume (Vol), and pore limiting diameter (PLD) of MOFs are the key features that affect the Xe/Kr adsorption property. To test the generalization ability of the XGBoost model, we also tried to screen MOF adsorbents on the CO 2 /CH 4 mixture, it is found that the prediction performance of XGBoost is also much better than that of the traditional machine learning models although with the unbalanced data. Note that the dimension of features of MOFs is low while the quantity of MOF samples in database is very large, which is suitable for the prediction by model such as XGBoost to search the global minimum of cost function rather than the model involving feature creation. The present study represents the first report using the XGBoost algorithm to discover the MOF adsorbates.

show abstract

Exploring the Structural, Dynamic, and Functional Properties of Metal‐Organic Frameworks through Molecular Modeling

Formalik,

Shi,

Joodaki

et al. 2023

Adv Funct Materials

View full text Add to dashboard Cite

This review spotlights the role of atomic‐level modeling in research on metal‐organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster‐based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high‐throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research.

show abstract

Large-scale computational assembly of ionic liquid/MOF composites: synergistic effect in the wire-tube conformation for efficient CO₂/CH₄ separation

Abstract: High-throughput construction of IL/MOF composites and discovery of the synergistic effect in the wire-tube conformation for efficient CO2/CH4 separation.

Cited by 54 publications

References 51 publications

Predicting hydrogen storage in MOFs via machine learning

Predicting hydrogen storage in MOFs via machine learning

XGBoost: An Optimal Machine Learning Model with Just Structural Features to Discover MOF Adsorbents of Xe/Kr

Exploring the Structural, Dynamic, and Functional Properties of Metal‐Organic Frameworks through Molecular Modeling

Contact Info

Product

Resources

About

Large-scale computational assembly of ionic liquid/MOF composites: synergistic effect in the wire-tube conformation for efficient CO2/CH4 separation

Abstract: High-throughput construction of IL/MOF composites and discovery of the synergistic effect in the wire-tube conformation for efficient CO2/CH4 separation.

Cited by 54 publications

References 51 publications

Predicting hydrogen storage in MOFs via machine learning

Predicting hydrogen storage in MOFs via machine learning

XGBoost: An Optimal Machine Learning Model with Just Structural Features to Discover MOF Adsorbents of Xe/Kr

Exploring the Structural, Dynamic, and Functional Properties of Metal‐Organic Frameworks through Molecular Modeling

Contact Info

Product

Resources

About

Large-scale computational assembly of ionic liquid/MOF composites: synergistic effect in the wire-tube conformation for efficient CO₂/CH₄ separation