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MOFIA: a chemoinformatic webserver for the prediction of CO2 adsorption in metal organic frameworks (MOF)

  • Michael Fernandez (a1), Tom D Daff (a1), Nicholas R. Trefiak (a1) and Tom K. Woo (a1)

Nanoporous metal-organic framework (MOF) materials are strong candidates for energy efficient carbon capture and storage (CCS) technologies. A total of ∼20,000 hypothetical MOFs were ab initio screened for CO2 adsorption using grand canonical Monte-Carlo (GCMC) simulations. Novel radial distribution function (RDF) scores were modified for periodic systems to predict the CO2 adsorption of MOFs using chemoinformatic models. The test set predictions yielded accuracies of 0.76 and 0.85 at 0.1 bar and 1 bar, respectively. The models were used to screen a large database for high performing MOFs and the top 100 structures were successfully validated by GCMC simulations. The chemoinformatic predictors of the CO2 adsorption of MOFs are available online at

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MRS Online Proceedings Library (OPL)
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  • EISSN: 1946-4274
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