Recommended by

Indexed by

Gas adsorption and process performance data for MOFs

Kevin Maik Jablonka1*, Andrew S. Rosen2,3,4, Berend Smit1*

1 Laboratory of Molecular Simulation (LSMO), École Polytechnique Fédérale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951 Sion, Switzerland

2 Department of Materials Science and Engineering, University of California, Berkeley, CA, 94720, USA

3 Miller Institute for Basic Research in Science, University of California, Berkeley, Berkeley, CA, 94720, USA

4 Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA

* Corresponding authors emails: mail@kjablonka.com, berend.smit@epfl.ch
DOI10.24435/materialscloud:qt-cj [version v1]

Publication date: Aug 11, 2022

How to cite this record

Kevin Maik Jablonka, Andrew S. Rosen, Berend Smit, Gas adsorption and process performance data for MOFs, Materials Cloud Archive 2022.101 (2022), doi: 10.24435/materialscloud:qt-cj.


Reticular chemistry provides materials designers with a practically infinite playground on different length scales. However, the space of all plausible materials for a given application is so large that it cannot be explored using a brute-force approach. One promising approach to guide the design and discovery of materials is machine learning, which typically involves learning a mapping of structures onto properties from data. To advance the data-driven materials discovery of metal-organic frameworks (MOFs) for gas storage and separation applications we provide a dataset of diverse gas separation properties (CO2, CH4, H2, N2, O2 isotherms); H2S, H2O, Kr, Xe Henry coefficients (computed using grand canonical Monte-Carlo with classical force fields) as well as parasitic energy for carbon capture from natural gas and a coal-fired power plant (computed using a simple process model) for the relaxed structures in the QMOF dataset with their DDEC charges.

Materials Cloud sections using this data

No Explore or Discover sections associated with this archive record.


File name Size Description
9.4 GiB Export of the AiiDA database of the molecular simulations.


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Preprint (Preprint in which the data is used.)
Kevin Maik Jablonka, Andrew S. Rosen, Berend Smit, in preparation.
Journal reference (Paper describing the input structures.)
A. S. Rosen, S. M. Iyer, D. Ray, Z. Yao, A. Aspuru-Guzik, L. Gagliardi, J. M. Notestein, and R. Q. Snurr, Machine Learning the Quantum-Chemical Properties of Metal–Organic Frameworks for Accelerated Materials Discovery, Matter 4, 1578 (2021) doi:10.1016/j.matt.2021.02.015
Journal reference (Paper describing the input structures.)
A. S. Rosen, V. Fung, P. Huck, C. T. O’Donnell, M. K. Horton, D. G. Truhlar, K. A. Persson, J. M. Notestein, and R. Q. Snurr, High-Throughput Predictions of Metal–Organic Framework Electronic Properties: Theoretical Challenges, Graph Neural Networks, and Data Exploration, Npj Computational Materials 8, (2022). doi:10.1038/s41524-022-00796-6
Website (Interactive explorer of the input structures.)


MOF ERC MARVEL nanoporous AiiDA

Version history:

2022.101 (version v1) [This version] Aug 11, 2022 DOI10.24435/materialscloud:qt-cj