CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening
Name
2573430.pdf
Description
Accepted version
Size
3.13 MB
Format
Adobe PDF
Checksum (MD5)
2579c6b508a7867d015e314c1534dd8b
Author(s)
Zhao, Guobin
Brabson, Logan M
Chheda, Saumil
Huang, Ju
Kim, Haewon
Liu, Kunhuan
Mochida, Kenji
Pham, Thang D
Prerna
Terrones, Gianmarco G
Date Issued
June 4, 2025
Journal
Matter
Publisher
Elsevier BV
Citation
CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening. Zhao, Guobin et al. Matter, Volume 8, Issue 6, 102140
Version
Author's final manuscript
Abstract
We present an updated version of the Computation-Ready, Experimental (CoRE) Metal-Organic Framework (MOF) database, which includes a curated set of computation-ready MOF crystal structures designed for high-throughput computational materials discovery. Data collection and curation procedures were improved from the previous version to enable more frequent updates in the future. Machine-learning-predicted properties, such as stability metrics and heat capacities, are included in the dataset to streamline screening activities. An updated version of MOFid was developed to provide detailed information on metal nodes, organic linkers, and topologies of an MOF structure. DDEC6 partial atomic charges of MOFs were assigned based on a machine-learning model. Gibbs ensemble Monte Carlo simulations were used to classify the hydrophobicity of MOFs. The finalized dataset was subsequently used to perform integrated material-process screening for various carbon-capture conditions using high-fidelity temperature-swing adsorption (TSA) simulations. Our workflow identified multiple MOF candidates that are predicted to outperform CALF-20 for these applications.
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Creative Commons Attribution-Noncommercial-ShareAlike
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DOI of Published Version
https://doi.org/10.1016/j.matt.2025.102140
