Methane Adsorption Database of MOFs for Machine Learning V3.0

Metal-organic frameworks (MOFs) possess high surface areas and customizable properties, making them among the most promising materials for gas adsorption. The structures of MOFs vary widely due to differences in metal nodes, organic linkers, and their combinations, with hundreds of thousands of distinct structures identified to date. Efficiently screening and designing MOFs with high storage capacities for gas adsorption is a key challenge in advancing MOFs and is critical for the development of carbon capture and energy storage technologies. This database includes 252,353 MOFs that can be directly utilized for training machine learning models. All these MOF structures are derived from public databases. The dataset features 14 geometric descriptors and 176 chemical descriptors Data for each MOF, along with methane adsorption capacities obtained from grand canonical Monte Carlo simulations at 5.8 bar and 65 bar, which are typical pressures for methane storage.
V2.0: The latest MOSAEC algorithm, developed by Woo et al., was employed to identify structures with impossible or unlikely metal oxidation states. These chemically invalid samples were subsequently removed from the dataset.
V3.0: RAC chemical descriptors were added in Chemical Descriptors Data.csv

Note: The following code is derived from the work of Woo et al. (DOI: https://doi.org/10.1021/jacs.5c04914). This code can be found online at https://github.com/uowoolab/MOSAEC. This file is uploaded solely to facilitate replication of the experiment. Please ensure that the original author's work is cited when reusing it.

Please cite this study if you use this dataset:
https://doi.org/10.1016/j.jece.2025.120263