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 3 million simulated adsorption data points for H2, CH4, CO2, Xe, Kr, Ar, and N2 in over 160 000 MOFs and 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 JSON format that 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. Here, 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.
- Authors:
-
[1];
[2];
[3];
[2];
[4];
[4];
[6];
[2]
- Northwestern University, Evanston, IL (United States); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Northwestern University, Evanston, IL (United States)
- Northwestern University, Evanston, IL (United States); University of Texas Rio Grande Valley, Edinburg, TX (United States)
- University of Minnesota, Minneapolis, MN (United States)
- Georgia Institute of Technology, Atlanta, GA (United States)
- National Institute of Standards and Technology, Gaithersburg, MD (United States)
- Publication Date:
- Research Org.:
- Northwestern Univ., Evanston, IL (United States); Univ. of Minnesota, Minneapolis, MN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB); USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 2311791
- Grant/Contract Number:
- SC0008688; FG02-17ER16362; NA0003525
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Chemical and Engineering Data
- Additional Journal Information:
- Journal Volume: 68; Journal Issue: 2; Journal ID: ISSN 0021-9568
- Publisher:
- American Chemical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
Bobbitt, N. Scott, Shi, Kaihang, Bucior, Benjamin J., Chen, Haoyuan, Tracy-Amoroso, Nathaniel, Li, Zhao, Sun, Yangzesheng, Merlin, Julia H., Siepmann, J. Ilja, Siderius, Daniel W., and Snurr, Randall Q. MOFX-DB: An Online Database of Computational Adsorption Data for Nanoporous Materials. United States: N. p., 2023.
Web. doi:10.1021/acs.jced.2c00583.
Bobbitt, N. Scott, Shi, Kaihang, Bucior, Benjamin J., Chen, Haoyuan, Tracy-Amoroso, Nathaniel, Li, Zhao, Sun, Yangzesheng, Merlin, Julia H., Siepmann, J. Ilja, Siderius, Daniel W., & Snurr, Randall Q. MOFX-DB: An Online Database of Computational Adsorption Data for Nanoporous Materials. United States. https://doi.org/10.1021/acs.jced.2c00583
Bobbitt, N. Scott, Shi, Kaihang, Bucior, Benjamin J., Chen, Haoyuan, Tracy-Amoroso, Nathaniel, Li, Zhao, Sun, Yangzesheng, Merlin, Julia H., Siepmann, J. Ilja, Siderius, Daniel W., and Snurr, Randall Q. Wed .
"MOFX-DB: An Online Database of Computational Adsorption Data for Nanoporous Materials". United States. https://doi.org/10.1021/acs.jced.2c00583. https://www.osti.gov/servlets/purl/2311791.
@article{osti_2311791,
title = {MOFX-DB: An Online Database of Computational Adsorption Data for Nanoporous Materials},
author = {Bobbitt, N. Scott and Shi, Kaihang and Bucior, Benjamin J. and Chen, Haoyuan and Tracy-Amoroso, Nathaniel and Li, Zhao and Sun, Yangzesheng and Merlin, Julia H. and Siepmann, J. Ilja and Siderius, Daniel W. and Snurr, Randall Q.},
abstractNote = {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 3 million simulated adsorption data points for H2, CH4, CO2, Xe, Kr, Ar, and N2 in over 160 000 MOFs and 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 JSON format that 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. Here, 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.},
doi = {10.1021/acs.jced.2c00583},
journal = {Journal of Chemical and Engineering Data},
number = 2,
volume = 68,
place = {United States},
year = {Wed Jan 04 00:00:00 EST 2023},
month = {Wed Jan 04 00:00:00 EST 2023}
}
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