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Deep Learning Combined with IAST to Screen Thermodynamically Feasible MOFs for Adsorption-Based Separation of Multiple Binary Mixtures

preprint
submitted on 26.02.2021, 06:23 and posted on 26.02.2021, 13:58 by Ryther Anderson, Diego Gómez-Gualdrón
This study demonstrates the coupling of a multipurpose multilayer perceptron (MLP) model that predicts single-component adsorption for a various molecules with ideal adsorption solution theory (IAST). The resulting computational framework predicts MOF separations properties for various binary mixtures at various compositions and pressures. The accuracy of the MLP+IAST framework was sufficiently high so that, for a given separation, MOFs in the 90th percentile from MLP+IAST-based screening contain ~87% of MOFs in the 95th percentile one would obtain from molecular simulation-based screening. Clustering algorithms were shown effective to identify so-called "privileged" MOFs that were high-performing for multiple separations. Free energy calculations were performed to determine privileged MOFs that were likely to be accesses synthetically, at least from a thermodynamic perspective.

Funding

NSF CBET 1846707

History

Email Address of Submitting Author

dgomezgualdron@mines.edu

Institution

Colorado School of Mines

Country

United States

ORCID For Submitting Author

0000-0003-3237-0199

Declaration of Conflict of Interest

The authors declare no conflict of interest

Version Notes

The following article has been submitted to The Journal of Chemical Physics

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