Document Type
Article
Publication Date
1-2023
Abstract
Machine learning and data mining coupled with molecular modeling have become powerful tools for materials discovery. Metal–organic frameworks (MOFs) are a rich area for this due to their modular construction and numerous applications. Here, we make data from several previous large-scale studies in MOFs and zeolites from our groups (and new data for N2 and Ar adsorption in MOFs) easily accessible in one place. The database includes over three million simulated adsorption data points for H2, CH4, CO2, Xe, Kr, Ar, and N2 in over 160 000 MOFs and 286 zeolites, textural properties like pore sizes and surface areas, and the structure file for each material. We include metadata about the Monte Carlo simulations to enable reproducibility. The database is searchable by MOF properties, and the data are stored in a standardized JavaScript Object Notation format that is interoperable with the NIST adsorption database. We also identify several MOFs that meet high performance targets for multiple applications, such as high storage capacity for both hydrogen and methane or high CO2 capacity plus good Xe/Kr selectivity. By providing this data publicly, we hope to facilitate machine learning studies on these materials, leading to new insights on adsorption in MOFs and zeolites.
Recommended Citation
Bobbitt, N. Scott, Kaihang Shi, Benjamin J. Bucior, Haoyuan Chen, Nathaniel Tracy-Amoroso, Zhao Li, Yangzesheng Sun et al. "MOFX-DB: An online database of computational adsorption data for nanoporous materials." Journal of Chemical & Engineering Data 68, no. 2 (2023): 483-498. https://doi.org/10.1021/acs.jced.2c00583
Publication Title
Journal of Chemical & Engineering Data
DOI
10.1021/acs.jced.2c00583
Comments
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