Analyzing acetylene adsorption of metal–organic frameworks based on machine learning

Yang, Pei‐Song; Lu, Gang; Yang, Qingyuan; Liu, Lei; Lai, Xin; Yu, Duli

doi:10.1016/j.gee.2021.01.006

Cited by 26 publications

(10 citation statements)

References 30 publications

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“…We also found that PLD is one of the most important factors among the seven structural features. The range of PLD for the top 25 MOFs is 5.20–9.67 Å, which indicates MOFs with suitable PLD can exhibit good gas separation performance (for more characters on the meaning, see refs , , ). The ranking of features could provide guidance for the development of novel MOFs with high SF 6 /N 2 separation performance (TSN).…”

Section: Resultsmentioning

confidence: 99%

Discovery of High-Performing Metal–Organic Frameworks for Efficient SF₆/N₂ Separation: A Combined Computational Screening, Machine Learning, and Experimental Study

Cao

Zhang

et al. 2023

Ind. Eng. Chem. Res.

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Effective capture and recovery of sulfur hexafluoride (SF 6 ) from SF 6 /N 2 mixture is an urgent challenge. Considering the existence of a large number of metal− organic frameworks (MOFs), the computational screening of MOFs is strongly desired before experimental efforts. In this work, the top-performance MOF adsorbents were identified from the most recent computation-ready, experimental metal−organic frameworks (CoRE MOFs) based on various metrics. The degree of unsaturation (unsat) and the number of hydrogen per unit cell (H) revealed with the optimal machine learning (ML) model are important factors for effective SF 6 /N 2 separation. One of the screened MOF candidates, FIRNAX01(TKL-107), was synthesized and the separation performance exceeded all the reported MOFs. Our computational screening not only offers effective prediction but also paves the way for accelerating the development of novel MOFs.

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Section: Resultsmentioning

confidence: 99%

Discovery of High-Performing Metal–Organic Frameworks for Efficient SF₆/N₂ Separation: A Combined Computational Screening, Machine Learning, and Experimental Study

Cao

Zhang

et al. 2023

Ind. Eng. Chem. Res.

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“…Such computational tools allow us to identify significant correlations between nanoscale features and observable macroscale properties 14,15 , and to select the most suitable crystal for a given application case. A few representative examples are provided by gas-gas separation (D 2 /H 2 16 , O 2 /N 2 17 , CO/N 2 18 , CO 2 /H 2 19 , ethane/ethylene 20 , and other gas mixtures 21 ), the enantioselectivity of chemical compounds 22 , gas adsorption (CO 2 23 , CH 4 24 , H 2 25 , thiol 26 , organosulfurs 27 , and acetylene 28 ), and combinations thereof 29,30 . Several computational explorations of MOFs datasets have been carried out also for biomedical (drug delivery 31 ), mechanical (CO 2 Brayton cycle 32 and osmotic heat engine 33 ), and energy applications (heat pumps/chillers 34,35 and thermal energy storage 36 ).…”

Section: Introductionmentioning

confidence: 99%

Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization

et al. 2022

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We focus on gas sorption within metal-organic frameworks (MOFs) for energy applications and identify the minimal set of crystallographic descriptors underpinning the most important properties of MOFs for CO2 and H2O. A comprehensive comparison of several sequential learning algorithms for MOFs properties optimization is performed and the role played by those descriptors is clarified. In energy transformations, thermodynamic limits of important figures of merit crucially depend on equilibrium properties in a wide range of sorbate coverage values, which is often only partially accessible, hence possibly preventing the computation of desired objective functions. We propose a fast procedure for optimizing specific energy in a closed sorption energy storage system with only access to a single water Henry coefficient value and to the specific surface area. We are thus able to identify hypothetical candidate MOFs that are predicted to outperform state-of-the-art water-sorbent pairs for thermal energy storage applications.

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“…This analysis is based on using the molecular structure of a material to predict its physical behavior. QSPR modeling has long been used particularly in the pharmaceutical industry for drug development, and it has recently seen expansion into various physical chemistry fields, including the adsorption of metal organic frameworks. − Additionally, the use of QSPR in combination with other computational methods such as DFT or process simulation has also garnered effective prediction within research in the energy field; , however, to the best of our knowledge, QSPR has not been applied to the development of a CSCM material for use in SE-SMR.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Combined Sorbent and Catalyst Materials for SE-SMR, Using QSPR and Multitask Learning

Nkulikiyinka

Wagland

Manović

et al. 2022

Ind. Eng. Chem. Res.

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The process of sorption enhanced steam methane reforming (SE-SMR) is an emerging technology for the production of low carbon hydrogen. The development of a suitable catalytic material, as well as a CO 2 adsorbent with high capture capacity, has slowed the upscaling of this process to date. In this study, to aid the development of a combined sorbent catalyst material (CSCM) for SE-SMR, a novel approach involving quantitative structure−property relationship analysis (QSPR) has been proposed. Through data-mining, two databases have been developed for the prediction of the last cycle capacity (g CO 2 /g sorbent ) and methane conversion (%). Multitask learning (MTL) was applied for the prediction of CSCM properties. Patterns in the data of this study have also yielded further insights; colored scatter plots were able to show certain patterns in the input data, as well as suggestions on how to develop an optimal material. With the results from the actual vs predicted plots collated, raw materials and synthesis conditions were proposed that could lead to the development of a CSCM that has good performance with respect to both the last cycle capacity and the methane conversion.

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Analyzing acetylene adsorption of metal–organic frameworks based on machine learning

Cited by 26 publications

References 30 publications

Discovery of High-Performing Metal–Organic Frameworks for Efficient SF₆/N₂ Separation: A Combined Computational Screening, Machine Learning, and Experimental Study

Discovery of High-Performing Metal–Organic Frameworks for Efficient SF₆/N₂ Separation: A Combined Computational Screening, Machine Learning, and Experimental Study

Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization

Prediction of Combined Sorbent and Catalyst Materials for SE-SMR, Using QSPR and Multitask Learning

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Analyzing acetylene adsorption of metal–organic frameworks based on machine learning

Cited by 26 publications

References 30 publications

Discovery of High-Performing Metal–Organic Frameworks for Efficient SF6/N2 Separation: A Combined Computational Screening, Machine Learning, and Experimental Study

Discovery of High-Performing Metal–Organic Frameworks for Efficient SF6/N2 Separation: A Combined Computational Screening, Machine Learning, and Experimental Study

Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization

Prediction of Combined Sorbent and Catalyst Materials for SE-SMR, Using QSPR and Multitask Learning

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Product

Resources

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Discovery of High-Performing Metal–Organic Frameworks for Efficient SF₆/N₂ Separation: A Combined Computational Screening, Machine Learning, and Experimental Study

Discovery of High-Performing Metal–Organic Frameworks for Efficient SF₆/N₂ Separation: A Combined Computational Screening, Machine Learning, and Experimental Study