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.