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

ABSTRACT: 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.

SUBMITTER: Daglar H

PROVIDER: S-EPMC9305976 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:To prevent greenhouse emissions into the atmosphere, separations like CO2/CH4 and CO2/N2 from natural gas, biogas, and flue gasses are crucial. Polymer membranes gained a key role in gas separations over the past decades, but these polymers are often not organized at a molecular level, which results in a trade-off between permeability and selectivity. In this work, the effect of molecular order and orientation in liquid crystals (LCs) polymer membranes for gas permeation is demonstrated. Using the self-assembly of polymerizable LCs to prepare membranes ensures control over the supramolecular organization and alignment of the building blocks at a molecular level. Robust freestanding LC membranes were fabricated that have various, distinct morphologies (isotropic, nematic cybotactic, and smectic C) and alignment (planar and homeotropic), while using the same chemical composition. Single gas permeation data show that the permeability decreases with increasing molecular order while the ideal gas selectivity of He and CO2 over N2 increases tremendously (36-fold for He/N2 and 21-fold for CO2/N2) when going from randomly ordered to the highly ordered smectic C morphology. The calculated diffusion coefficients showed a 10-fold decrease when going from randomly ordered membranes to ordered smectic C membranes. It is proposed that with increasing molecular order, the free volume elements in the membrane become smaller, which hinders gasses with larger kinetic diameters (Ar, N2) more than gasses with smaller kinetic diameters (He, CO2), inducing selectivity. Comparison of gas sorption and permeation performances of planar and homeotropic aligned smectic C membranes shows the effect of molecular orientation by a 3-fold decrease of the diffusion coefficient of homeotropic aligned smectic C membranes resulting in a diminished gas permeation and increased ideal gas selectivities. These results strongly highlight the importance of molecular order and orientation in LC polymer membranes for gas separation.

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

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