MOFIA: a chemoinformatic webserver for the prediction of CO 2 adsorption in metal organic frameworks (MOF)
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MOFIA: a chemoinformatic webserver for the prediction of CO2 adsorption in metal organic frameworks (MOF) Michael Fernandez, Tom D Daff, Nicholas R. Trefiak and Tom K. Woo Centre for Catalysis Research and Innovation, Department of Chemistry, University of Ottawa, Ottawa, Canada ABSTRACT 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 http://titan.chem.uottawa.ca/woolab/MOFIA/#carbondioxide. INTRODUCTION Nanoporous metal-organic framework (MOF) materials are a class of porous solids with exceptional host-guest properties that possess ‘world-record’ internal surface areas of >6000 m2/g.1 Since they can selectively adsorb CO2, they are considered serious contenders for viable carbon capture and storage (CCS) technologies. MOFs are formed by the self-assembly of, metal ions or clusters and polydentate organic linkers that function as structural building units (SBU) to create open, crystalline frameworks.1 Due to technological limitations, only a handful of high-throughput (HT) experiments are available on hydrothermal stability2 and gas adsorption capabilities3 of MOFs.4,5 In contrast, grand canonical Monte-Carlo (GCMC) simulations6,7 of gas adsorption isotherms3,8 have recently enabled the in-silico HT screening of gas adsorption data of porous materials. The analysis of such large-scale data sets demands sophisticated chemoinformatic methods, which provide valuable insights into structure-property relationships. Chemoinformatic methods can retrieve information from HT experiments on organic molecules using structural similarity approaches.9 However, very few of these approaches have been extended to periodic crystal structures. Neither have specialized structural descriptors been systematically implemented to detect similarities in shape, topology or electronic structure of porous solid materials. Upon the development of descriptor metrics suitable for MOFs, predictive chemoinformatic models10,11 can correlate structural features to functional properties in quantitative terms yielding comprehensive and interpretable predictors. In this paper, we present a comprehensive chemoinformatic modeling of the CO2 adsorption capacity of MOFs. A total of ~20,000 MOFs from the Northwestern University database12 were ab initio screened for CO2 adsorption at pressures of 0.1 bar and 1 bar. The collected absorption data was investigated
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