TY - JOUR
T1 - MOFX-DB
T2 - An Online Database of Computational Adsorption Data for Nanoporous Materials
AU - Bobbitt, N. Scott
AU - Shi, Kaihang
AU - Bucior, Benjamin J.
AU - Chen, Haoyuan
AU - Tracy-Amoroso, Nathaniel
AU - Li, Zhao
AU - Sun, Yangzesheng
AU - Merlin, Julia H.
AU - Siepmann, J. Ilja
AU - Siderius, Daniel W.
AU - Snurr, Randall Q.
N1 - Funding Information:
This research was supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under award DE-FG02-17ER16362. The authors thank Thang Pham for assistance with the units conversion function in MOFX-DB. Official contribution of the National Institute of Standards and Technology (NIST), not subject to copyright in the United States of America. Certain commercially available items may be identified in this paper. This identification neither does imply recommendation by NIST nor does it imply that it is the best available for the purposes described. Development of the NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E) through Interagency Agreement Number 1208-0000. Additional computer resources were provided by the Minnesota Supercomputing Institute at the University of Minnesota. H.C. acknowledges the start-up funding from The University of Texas Rio Grande Valley. This work was funded by the Laboratory Directed Research and Development program at Sandia National Laboratories. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This article has been authored by an employee of National Technology & Engineering Solutions of Sandia, LLC under contract no. DE-NA0003525 with the U.S. Department of Energy (DOE). The employee owns all right, title, and interest in and to the article and is solely responsible for its contents. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this article or allow others to do so, for United States Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan https://www.energy.gov/downloads/doe-public-access-plan . This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.
Funding Information:
This research was supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under award DE-FG02-17ER16362. The authors thank Thang Pham for assistance with the units conversion function in MOFX-DB. Official contribution of the National Institute of Standards and Technology (NIST), not subject to copyright in the United States of America. Certain commercially available items may be identified in this paper. This identification neither does imply recommendation by NIST nor does it imply that it is the best available for the purposes described. Development of the NIST/ARPA-E Database of Novel and Emerging Adsorbent Materials was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E) through Interagency Agreement Number 1208-0000. Additional computer resources were provided by the Minnesota Supercomputing Institute at the University of Minnesota. H.C. acknowledges the start-up funding from The University of Texas Rio Grande Valley. This work was funded by the Laboratory Directed Research and Development program at Sandia National Laboratories. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This article has been authored by an employee of National Technology & Engineering Solutions of Sandia, LLC under contract no. DE-NA0003525 with the U.S. Department of Energy (DOE). The employee owns all right, title, and interest in and to the article and is solely responsible for its contents. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this article or allow others to do so, for United States Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan https://www.energy.gov/downloads/doe-public-access-plan. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.
Publisher Copyright:
© 2023 American Chemical Society.
PY - 2023/2/9
Y1 - 2023/2/9
N2 - 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.
AB - 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.
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U2 - 10.1021/acs.jced.2c00583
DO - 10.1021/acs.jced.2c00583
M3 - Article
AN - SCOPUS:85146017970
SN - 0021-9568
VL - 68
SP - 483
EP - 498
JO - Journal of Chemical & Engineering Data
JF - Journal of Chemical & Engineering Data
IS - 2
ER -