This repository hosts the Quantum MOF (QMOF) database as described in: https://doi.org/10.1016/j.matt.2021.02.015.
IMPORTANT NOTE:
Please visit https://materialsproject.org/mofs, which is the landing page for the QMOF Database and contains further information about this dataset.
See the documentation for information on how to download other files and data: https://materialsproject.gitbook.io/materials-project-public-docs/methodology/mof-explorer/downloading-the-data
If you use or wish to cite the QMOF Database, please refer to the following publications:
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. (2022). DOI: 10.1038/s41524-022-00796-6.
Funding
National Defense Science and Engineering Graduate Fellowship
Nanoporous Materials Genome Center (DE-FG02-17ER16362)