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

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.


NSF CBET 1846707


Email Address of Submitting Author


Colorado School of Mines


United States

ORCID For Submitting Author


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