A full set of flexible force field parameters for ZIF-8, applied for its gas adsorption and diffusion, has been presented.

Metal–organic frameworks (MOFs) are one category of emerging porous materials, which are promising competitors applied in gas storage and separation due to their high porosity and high surface area. It is still time consuming to search for optimal materials for methane storage from a large number of candidates by traditional methods such as molecular simulations and quantum mechanics. Recently, machine learning (ML) algorithms were gradually used to accelerate the discovery of high-performance MOFs. In this work, Henry’s coefficient besides other characteristic parameters was computed and appended into the previously reported data set of hypothetical metal–organic frameworks (hMOFs) for methane storage. The new data set with 37 features and 130 397 samples was then randomly split into a training set and a test set in the ratio of 7:3, which were applied for ML training and testing with three different algorithms, including support vector machine, random forest regression (RFR), and gradient boosting regression tree (GBRT). The results indicate that the GBRT model demonstrates the best generalization ability to predict nontrained data set, whereas the RFR model results in the best predictive power in the training set. The analysis of feature importance from machine learning algorithms confirms that the high generalization ability of the GBRT model is attributed to the model extracting more information from a wider range of features. The RFR model results in the highest prediction accuracy with Pearson correlation coefficient (r 2) of 0.9984 and root mean square error (RMSE) of 3.93 in the training set of absolute gravimetric uptakes. The GBRT model results in the highest prediction accuracy with r 2 of 0.9908 and RMSE of 9.40 in the test set of absolute gravimetric uptakes, which is the highest prediction accuracy among the up-to-date reports. According to volumetric capacities for methane storage, the optimal hMOFs exhibit ϕ of 0.65–0.88, liquid-crystal display of ∼7.5 Å, VSA of ∼2250 m2 cm–3, etc.

To seek new potential materials for hydrogen storage, an arc-discharge method was employed to prepare nanosized nickel(or cobalt)/graphite composites, in which the nickel (or cobalt) particles were highly dispersed in a carbon matrix with particle size between 20 and 70 nm (or 5-20 nm). Quantitative TPD measurements showed that at about 500 °C and 30-50 atm these nanosized composites could uptake up to 2.8 wt % H 2 , which can be released at 500 °C and 1 atm. The addition of Ni (or Co) in C was found to largely enhance the H 2 adsorption, with the optimal amount of Ni being 20 wt %. In-situ FTIR showed that hydrogen was dissociatively adsorbed only in the presence of a transition metal and bonded to carbon atoms forming C-H bond. The hydrogen adsorption/desorption could be recycled. However, the capacity decreased to 1.6 wt % after 5 cycles. TEM, XPS, and BET surface-area and pore-volume measurements revealed that some of the transition metal particles migrated out from the carbon matrix and agglomerated after the H 2 adsorption/ desorption cycles, which may reduce the transition metal-carbon synergism and thus the H 2 storage capacity. Under low temperatures below -120 °C and moderate pressures above 6 atm hydrogen storage by these Ni(or Co)/C composites could be detected. Storage capacity up to 2.7 wt % for Ni/C was measured by PCI at 77 K and 70 atm.

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We proposed four novel PAF materials with extremely low density and unprecedented high free volume ratio, which were predicted to possess ultrahigh gravimetric hydrogen uptake reaching the DOE 2015 gravimetric targets at room temperature based on GCMC simulation calculations.

A large-scale computational screening of 13 512 MOFs with topological diversity was carried out to search the optimal candidates for the simultaneous separation of two dimethyl butanes from the quinary equimolar mixture of hexane isomers.

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