Predicting of Covalent Organic Frameworks for Membrane-based Isobutene/1,3-Butadiene Separation: Combining Molecular Simulation and Machine Learning

Cao, Xiaohao; He, Yanjing; Zhang, Zhengqing; Sun, Yuxiu; Han, Qiang; Guo, Yandong; Zhong, Chongli

doi:10.1007/s40242-022-1452-z

Cited by 12 publications

(8 citation statements)

References 32 publications

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“…Qiao et al used the ML approach to compute the relative importance of MOF features on the predicted membrane selectivities and showed that porosity and the largest cavity diameter (LCD) have high importance. Zhong et al developed an ML model to predict i -C 4 H 8 permeability and i -C 4 H 8 /C 4 H 6 selectivity of 601 covalent organic framework (COF) membranes at 1 bar, 298 K, and showed that porosity and pore limiting diameter (PLD) are key factors controlling the selectivity and permeability of COF membranes. Bai et al recently developed eight different ML algorithms to predict H 2 permeability, H 2 /CH 4 membrane selectivity, and trade-off multiple selectivity and permeability (TMSP) of MOFs and showed that two ML models are the most suitable ones for predicting the H 2 separation performances of MOFs.…”

Section: Introductionmentioning

confidence: 99%

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

Daglar

Keskin

2022

ACS Appl. Mater. Interfaces

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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.

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Section: Introductionmentioning

confidence: 99%

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

Daglar

Keskin

2022

ACS Appl. Mater. Interfaces

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“…357 Cao et al combined machine learning with molecular separations to identify COFs for membrane separation using membrane performance score (MPS) and selectivity. 289 Desgranges et al used ensemble ML methodology to predict the partition function of fluids adsorbed in MOFs and COFs. 358 The prediction of partition function over a wide range of conditions allows access to thermodynamic properties of adsorption, such as adsorption isotherm, entropy, and Gibbs free energy of the adsorbed fluid.…”

Section: Processes For Carbon Capture Using Cofs: Developments and Ch...mentioning

confidence: 99%

Covalent organic frameworks for CO₂ capture: from laboratory curiosity to industry implementation

Li,

Dilipkumar,

Abubakar

et al. 2023

Chem. Soc. Rev.

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“…Fanourgakis et al 33 employed a self-consistent ML approach to decrease the cost of molecular simulations and identified the top COFs for CH 4 storage among 69,840 hypoCOFs. Cao et al 34 35 Recently, our group developed ML models to screen CoRE COFs and hypoCOFs for CH 4 /H 2 separation and showed that pore size and heat of adsorption of gases are the main factors determining the separation performance of COFs. 36 Motivated from the great potential of harnessing HTCS and ML methods to examine a large number and variety of materials, in this work, we evaluated all synthesized and hypothetical COFs, 613 CoRE COFs and 69,840 hypoCOFs, for CO 2 /CH 4 separation under four different processes: PSA, VSA, TSA, and PTSA.…”

Section: Introductionmentioning

confidence: 99%

Rapid and Accurate Screening of the COF Space for Natural Gas Purification: COFInformatics

Aksu,

Keskin

2024

ACS Appl. Mater. Interfaces

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In this work, we introduced COFInformatics, a computational approach merging molecular simulations and machine learning (ML) algorithms, to evaluate all synthesized and hypothetical covalent organic frameworks (COFs) for the CO 2 /CH 4 mixture separation under four different adsorptionbased processes: pressure swing adsorption (PSA), vacuum swing adsorption (VSA), temperature swing adsorption (TSA), and pressure−temperature swing adsorption (PTSA). We first extracted structural, chemical, energy-based, and graph-based molecular fingerprint features of every single COF structure in the very large COF space, consisting of nearly 70,000 materials, and then performed grand canonical Monte Carlo simulations to calculate the CO 2 /CH 4 mixture adsorption properties of 7540 COFs. These features and simulation results were used to develop ML models that accurately and rapidly predict CO 2 /CH 4 mixture adsorption and separation properties of all 68,614 COFs. The most efficient separation process and the best adsorbent candidates among the entire COF spectrum were identified and analyzed in detail to reveal the most important molecular features that lead to high-performance adsorbents. Our results showed that (i) many hypoCOFs outperform synthesized COFs by achieving higher CO 2 /CH 4 selectivities; (ii) the top COF adsorbents consist of narrow pores and linkers comprising aromatic, triazine, and halogen groups; and (iii) PTSA is the most efficient process to use COF adsorbents for natural gas purification. We believe that COFInformatics promises to expedite the evaluation of COF adsorbents for CO 2 /CH 4 separation, thereby circumventing the extensive, time-and resource-intensive molecular simulations.

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Predicting of Covalent Organic Frameworks for Membrane-based Isobutene/1,3-Butadiene Separation: Combining Molecular Simulation and Machine Learning

Cited by 12 publications

References 32 publications

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

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

Covalent organic frameworks for CO₂ capture: from laboratory curiosity to industry implementation

Rapid and Accurate Screening of the COF Space for Natural Gas Purification: COFInformatics

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Predicting of Covalent Organic Frameworks for Membrane-based Isobutene/1,3-Butadiene Separation: Combining Molecular Simulation and Machine Learning

Cited by 12 publications

References 32 publications

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

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

Covalent organic frameworks for CO2 capture: from laboratory curiosity to industry implementation

Rapid and Accurate Screening of the COF Space for Natural Gas Purification: COFInformatics

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Product

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Covalent organic frameworks for CO₂ capture: from laboratory curiosity to industry implementation