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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.

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