Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2019 Dataset

High-throughput computational screening of metal-organic frameworks rely on the availability of atomic coordinate files which can be used as input to simulation software packages. CoRE MOF Datasets are derived from Cambridge Structural Database (CSD) and also from the World Wide Web.

Nomenclatures:

LCD (Largest Cavity Diameter), PLD (pore limiting diameter), LFPD (Largest Sphere along the Free Path), ASA (Accessible Surface Area), NASA (Non-accessible surface area), AV_VF (Void Fraction, 0 - 1), NAV (Non Accessible Volume)

Dataset Directory Organization

CoREMOF2019_public_v2.zip: dataset with CR and NCR classifications

1.  CR dataset: computaion-ready (N = 10,367)

    ASR: all solvent removed (N = 6,603)

    FSR: free solvent removed (N = 3,764)

2.  NCR: not computaion-ready (N = 8,714)

    ASR: all solvent removed (N = 5,417)

Both: NCR determined by Chen_Manz and mofchecker (N = 2,597)

Chen_Manz: NCR determined by Chen_Manz (N = 958)

mofchecker: NCR determined by mofchecker (N = 1,859)

PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (N = 3)

    FSR: free solvent removed (N = 3,297)

Both: NCR determined by Chen_Manz and mofchecker (N = 1,646)

Chen_Manz: NCR determined by Chen_Manz (N = 463)

mofchecker: NCR determined by mofchecker (N = 1,185)

PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (N = 3)

2. NCR_detail.xlsx: details of all structures by mofchecker and Chen_Manz for each NCR cases

November, 24 2024

Re-ordering of folders such that top level directory is based on computation-ready and not-computation ready classification.

November, 13 2024

Classification of Computation-Ready (CR) and Not Computation-Ready (NCR) Structures based on Chen & Manz (RSC Adv., 2020,10, 26944-26951) and MOFChecker program by Kevin M. Jablonka)

ML-predicted DDEC6 partial atomic charges based on PACMAN

Acknowledgements

This reserach is supported by the National Research Foundation of Korea (No. 2016R1D1A1B3934484, NRF-2020R1C1C1010373, RS-2024-00449431)

This research is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-17ER16362 (Predictive Hierarchical Modeling of Chemical Separations and Transformations in Functional Nanoporous Materials: Synergy of Electronic Structure Theory, Molecular Simulations, Machine Learning, and Experiment)