MOFX-DB: An Online Database of Computational Adsorption Data for Nanoporous Materials

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.

Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials

“…(Originally, 13512 MOFs were reported, but one of them contains no atoms. We excluded this null structure here and in the latest MOFX-DB collection .…”

“…See Table . There is a total of 13511 MOFs of 41 different topologies in the ToBaCCo 1.0 database. (Originally, 13512 MOFs were reported, but one of them contains no atoms.…”

“…We excluded this null structure here and in the latest MOFX-DB collection. 45 ) The variety of topologies in the ToBaCCo MOFs also leads to a wide distribution in pore size, void fraction, and surface area. 35 All ToBaCCo MOF structures were downloaded from the MOFX-DB Web site (https://mof.tech.northwestern.edu/, accessed Nov. 15, 2020), 45 and we refer to MOFs following the same naming convention as in the MOFX-DB.…”

High-Throughput Computational Screening and Machine Learning Model for Accelerated Metal–Organic Frameworks Discovery in Toluene Vapor Adsorption

“…A recently developed all-solvent-removed CoRE MOF database by Snurr et al , , which contains 11,937 MOFs derived directly from the experimental data, was first used for high-throughput GCMC calculations and ML model development. In addition, a hypothetical MOF database developed by Wilmer, which contains 137,953 MOFs, was used for virtual screening.…”

GHP-MOFassemble: Diffusion modeling, high throughput screening, and molecular dynamics for rational discovery of novel metal-organic frameworks for carbon capture at scale

“…• Decompose. We use a molecular fragmentation algorithm to decompose the MOF linkers found in high-performing MOFs within the hMOF dataset [26]-an open source dataset that provides, for each of 137,652 hypothetical MOF structures, its MOFid, MOFkey, geometric features, and isotherm data for six adsorption gases (CO 2 , N 2 , CH 4 , H 2 , Kr, Xe) at 0.01, 0.05, 0.1, 0.5, and 2.5 bar-covering the pressure ranges of cyclic adsorption gas separation processes in industrial applications. The adsorption properties of gases provided in this dataset were calculated using Grand Canonical Monte Carlo (GCMC) calculations [26], as described in Wilmer et al [38].…”