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

Publication date: Aug 11, 2022

How to cite this record

Description

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

Files

License

External references

Keywords