Machine-Learning-Assisted High-Throughput computational screening of Metal–Organic framework membranes for hydrogen separation

Bai, Xiangning; Shi, Zenan; Xia, Huan; Li, Shuhua; Liu, Zili; Liang, Hong; Liu, Zhiting; Wang, Bangfen; Qiao, Zhiwei

doi:10.1016/j.cej.2022.136783

Cited by 33 publications

(18 citation statements)

References 45 publications

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“…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. In our recent study, ML models were trained to predict O 2 /N 2 adsorption, diffusion, and membrane selectivities of 5632 MOFs and 137,953 hypothetical MOFs (hMOFs) at 1 bar, 298 K, to identify the hMOFs with high O 2 /N 2 selectivity …”

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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“…Due to the variety of membrane backbone materials and available additives as well as the complexity of the fabrication process, rationally designing UF membranes by developing efficient methods to alleviate the time and resource constraints posed by the iterative trial-and-error approach is pivotal. Because of its powerful ability in processing and learning from large, complex, and multidimensional data sets to develop predictive models, machine learning (ML) as a data-driven method has become increasingly important in chemistry and material science communities for accelerating the discovery of new materials and chemical synthesis. − In most recent years, ML has been employed to guide gas separation membrane design. − In addition, ML models such as tree-based models (e.g., random forest, XGBoost, etc.) and artificial neural networks (ANNs) have been developed to predict permeance and rejection for RO and NF membranes in water treatment and resource recovery, including but not limited to solvent recovery. − Several studies have been acknowledged for the application of ML models in ML-assisted UF membrane fabrication. , However, the performance of these reported models was often limited by the incomplete input variables and unclear classification of the input features.…”

Section: Introductionmentioning

confidence: 99%

Understanding and Designing a High-Performance Ultrafiltration Membrane Using Machine Learning

Gao

Zhong

Dangayach

et al. 2023

Environ. Sci. Technol.

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Ultrafiltration (UF) as one of the mainstream membrane-based technologies has been widely used in water and wastewater treatment. Increasing demand for clean and safe water requires the rational design of UF membranes with antifouling potential, while maintaining high water permeability and removal efficiency. This work employed a machine learning (ML) method to establish and understand the correlation of five membrane performance indices as well as three major performance-determining membrane properties with membrane fabrication conditions. The loading of additives, specifically nanomaterials (A_wt %), at loading amounts of >1.0 wt % was found to be the most significant feature affecting all of the membrane performance indices. The polymer content (P_wt %), molecular weight of the pore maker (M_Da), and pore maker content (M_wt %) also made considerable contributions to predicting membrane performance. Notably, M_Da was more important than M_wt % for predicting membrane performance. The feature analysis of ML models in terms of membrane properties (i.e., mean pore size, overall porosity, and contact angle) provided an unequivocal explanation of the effects of fabrication conditions on membrane performance. Our approach can provide practical aid in guiding the design of fit-for-purpose separation membranes through data-driven virtual experiments.

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“…17 Metal–organic frameworks (MOFs) as periodically ordered porous materials are used for the separation film design due to their highly designable crystal structure and high specific surface area. 18 Sun et al prepared ZIF-67 derivative composite films with a permeate flux of 75.16 L m −2 h −1 bar −1 and 99% dye rejection. 19 Moreover, many MOFs with water stability (MIL-53, 20 MIL-125, 21 UiO-66, 22 ZIF-7, 23 ZIF-8, 24 ZIF-68, 25 etc. )…”

Section: Introductionmentioning

confidence: 99%

Efficient and selective film separation of organism/salt with graded nanofluid channels stimulated by a rigid crystal skeleton

Zhang

Tan

et al. 2023

J. Mater. Chem. A

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Machine-Learning-Assisted High-Throughput computational screening of Metal–Organic framework membranes for hydrogen separation

Cited by 33 publications

References 45 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

Understanding and Designing a High-Performance Ultrafiltration Membrane Using Machine Learning

Efficient and selective film separation of organism/salt with graded nanofluid channels stimulated by a rigid crystal skeleton

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