DOE OSTI · 3010677
CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening
Abstract
Here, 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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Zhao, Guobin [Pusan National University, Busan (South Korea)], Brabson, Logan M. [Georgia Institute of Technology, Atlanta, GA (United States)], Chheda, Saumil [University of Minnesota, Minneapolis, MN (United States); University of California, Berkeley, CA (United States)], Huang, Ju [University of Toronto, ON (Canada)], Kim, Haewon [Pusan National University, Busan (South Korea)], Liu, Kunhuan [Northwestern University, Evanston, IL (United States)], Mochida, Kenji [Northwestern University, Evanston, IL (United States)], Pham, Thang D., None, Prerna [University of Minnesota, Minneapolis, MN (United States)], Terrones, Gianmarco G. [Massachusetts Institute of Technology, Cambridge, MA (United States)], Yoon, Sunghyun [Pusan National University, Busan (South Korea)], Zoubritzky, Lionel [Institut de Recherche de Chimie Paris (France); Air Liquide, Les Loges-en-Josas (France)], Coudert, François-Xavier [Institut de Recherche de Chimie Paris (France)], Haranczyk, Maciej [IMDEA Materials Institute, Madrid (Spain)], Kulik, Heather J. [Massachusetts Institute of Technology, Cambridge, MA (United States)] (ORCID:0000000193420191), Moosavi, Seyed Mohamad [University of Toronto, ON (Canada)], Sholl, David S. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)], Siepmann, J. Ilja [University of Minnesota, Minneapolis, MN (United States)], Snurr, Randall Q. [Northwestern University, Evanston, IL (United States)], Chung, Yongchul G. [Pusan National University, Busan (South Korea)] (ORCID:0000000277560589). 2025-05-05. CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening. https://doi.org/10.1016/j.matt.2025.102140
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