https://github.com/andrew-s-rosen/qmof
The QMOF Database: A database of quantum-mechanical properties for metal-organic frameworks.
https://github.com/andrew-s-rosen/qmof
Last synced: over 1 year ago
JSON representation
The QMOF Database: A database of quantum-mechanical properties for metal-organic frameworks.
- Host: GitHub
- URL: https://github.com/andrew-s-rosen/qmof
- Owner: Andrew-S-Rosen
- License: mit
- Created: 2020-10-27T18:44:22.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2025-02-18T02:37:24.000Z (over 1 year ago)
- Last Synced: 2025-03-29T02:06:00.764Z (over 1 year ago)
- Language: Python
- Homepage:
- Size: 43.5 MB
- Stars: 139
- Watchers: 5
- Forks: 26
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# QMOF Database
## Overview
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.
## Explore and Download the QMOF Database
Much of the data underlying the QMOF Database can be downloaded from [Figshare](https://doi.org/10.6084/m9.figshare.13147324). For additional documentation and supplemental data, refer to the following:
## Updates
For a list of version-specific updates, see [updates.md](https://github.com/arosen93/QMOF/blob/main/updates.md).
## FAQs
- Q: Are trajectories avaialable with the QMOF Database? A: No.
## Citation
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](https://doi.org/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](https://doi.org/10.1038/s41524-022-00796-6).
## Licensing
The data underlying the QMOF Database is made publicly available under a [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/). This means you can copy it, share it, adapt it, and do whatever you like with it provided that you give [appropriate credit](https://wiki.creativecommons.org/wiki/License_Versions#Detailed_attribution_comparison_chart) and [indicate any changes](https://wiki.creativecommons.org/wiki/License_Versions#Modifications_and_adaptations_must_be_marked_as_such).