The Quantum MOF (QMOF) Database is a publicly available dataset of quantum-chemical properties for 20,000+ metal–organic frameworks (MOFs) and coordination polymers derived from high-throughput periodic density functional theory (DFT) calculations. The MOFs are all DFT-optimized and are derived from a variety of parent databases, including both experimental and hypothetical MOF databases.
Explore the dataset and more at the following link:
https://materialsproject.org/mofs
To download the data underlying the QMOF Database (i.e. DFT-optimized geometries, energies, partial atomic charges, bond orders, atomic spin densities, magnetic moments, band gaps, charge densities, density of states, etc. as well as the raw VASP files), see the documentation below:
Documentation: Downloading the QMOF Database
Follow the QMOF Database on Twitter (@QMOF_Database) if you want to be the first to know about the latest news and updates.
For a list of version-specific updates, see updates.md.
If you use the QMOF Database, please refer to the following publications. Both should be cited if you are using the dataset with 20k+ structures.
- A.S. Rosen, S.M. Iyer, D. Ray, Z. Yao, A. Aspuru-Guzik, L. Gagliardi, J.M. Notestein, R.Q. Snurr. "Machine Learning the Quantum-Chemical Properties of Metal–Organic Frameworks for Accelerated Materials Discovery", Matter, 4, 1578-1597 (2021). DOI: 10.1016/j.matt.2021.02.015.
- A.S. Rosen, V. Fung, P. Huck, C.T. O'Donnell, M.K. Horton, D.G. Truhlar, K.A. Persson, J.M. Notestein, R.Q. Snurr. "High-Throughput Predictions of Metal–Organic Framework Electronic Properties: Theoretical Challenges, Graph Neural Networks, and Data Exploration," npj Comput. Mat., 8, 112 (2022). DOI: 10.1038/s41524-022-00796-6.
The data underlying the QMOF Database is made publicly available under a CC BY 4.0 license. This means you can copy it, share it, adapt it, and do whatever you like with it provided that you give appropriate credit and indicate any changes.
If you have any questions, you can reach the corresponding author at the e-mail listed here.
- V. Fung, J. Zhang, E. Juarez, B.G. Sumpter, npj Comput. Mater. (2021). https://doi.org/10.1038/s41524-021-00554-0
- S.M. Moosavi, B.A. Novotny, D. Ongari, E. Moubarak, M. Asgari, O. Kadioglu, C. Charalambous, A. Ortega-Guerrero, A.H. Farmahini, L. Sarkisov, S. Garcia, G. Noé, B. Smit, Nat. Mater. (2022). https://doi.org/10.1038/s41563-022-01374-3
- H.U. Escobar- Hernandez, L.M. Perez, P. Hu, F.A. Soto, M.I. Papadaki, H.-C. Zhou, Q. Wang, Ind. Eng. Chem. Res. (2022). https://doi.org/10.1021/acs.iecr.2c00561
- S.S. Omee, S.-Y. Louis, N. Fu, L. Wei, S. Dey, R. Dong, Q. Li, J. Hu, Patterns (2022). https://doi.org/10.1016/j.patter.2022.100491
- X. Wu, Y. Che, L. Chen, E.J. Amigues, R. Wang, J. He, H. Dong, L. Ding, ACS Appl. Mater. Interfaces (2022). https://pubs.acs.org/doi/10.1021/acsami.2c10861.
- K. Choudhary, T. Yildrim, D.W. Siderius, A.G. Kusne, A. McDannald, D.L. Ortiz-Montalvo, Comput. Mater. Sci (2022). https://doi.org/10.1016/j.commatsci.2022.111388
- V. Korolev, I. Nevolin, P. Protsenko, Sci. Rep. (2022). https://doi.org/10.1038/s41598-022-19205-5
- S.A. Tawfik, S.P. Russo, J. Cheminform. (2022). https://doi.org/10.1186/s13321-022-00658-9
- Z. Cao, R. Magar, Y. Wang, A.B. Farimani, arXiv (2022). https://doi.org/10.48550/arXiv.2210.14188
- S. Wang, X. Guo, L. Zhao, arXiv (2022). https://doi.org/10.48550/arXiv.2201.11932
- K.M. Jablonka, A.S. Rosen, A.S. Krishnapriyan, B. Smit, ChemRxiv (2022). https://doi.org/10.26434/chemrxiv-2022-4g7rx
- Y. Kang, H. Park, B. Smit, J. Kim, ChemRxiv (2022). https://doi.org/10.26434/chemrxiv-2022-hcjzc
- Y. Liu, X. Liu, B. Cao, Research Square (2022). https://doi.org/10.21203/rs.3.rs-1064739/v1
- J. I. Gómez-Peralta, X. Bokhimi, N. G. García-Peña, P. Quintana-Owen, G. Rodríguez-Gattorno, J. Appl. Cryst. (2022). https://doi.org/10.1107/S1600576722009797