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Recent experiments show that the diffusivities of C8 and C10 n‐alkanes in ZIF‐8 are higher than those of C7 and C9, respectively. We investigated this unusual 'odd‐even' effect by simulating the adsorption of C7‐C14 n‐alkanes in ZIF‐8 using hybrid Monte Carlo molecular simulations. The resultant adsorption isotherms, guest‐host energies, isosteric heats, and chain length distributions are analyzed for trends among the n‐alkanes. ZIF‐8 cages filled with n‐alkanes are characterized using a combination of image processing and data science to quantify the differences in packing that occur with chain length. Results indicate that packing changes drastically from C7 to C8 and from C9 to C10, which is consistent with the diffusion trends previously reported in experiments. [ABSTRACT FROM AUTHOR]

A machine learning (ML) methodology that uses a histogram of interaction energies has been applied to predict gas adsorption in metal–organic frameworks (MOFs) using results from atomistic grand canonical Monte Carlo (GCMC) simulations as training and test data. In this work, the method is first extended to binary mixtures of spherical species, in particular, Xe and Kr. In addition, it is shown that single-component adsorption of ethane and propane can be predicted in good agreement with GCMC simulation using a histogram of the adsorption energies felt by a methyl probe in conjunction with the random forest ML method. The results for propane can be improved by including a small number of MOF textural properties as descriptors. We also discuss the most significant features, which provides physical insight into the most beneficial adsorption energy sites for a given application. [ABSTRACT FROM AUTHOR]