materialscloud:2022.101
Published August 11, 2022 | Version v1
Dataset Open

Gas adsorption and process performance data for MOFs

Creators

  • 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

* Contact person

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.

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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.)
A. S. Rosen et al. Materials Project MOF app