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

26 February 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


machine learning
chemical separation
free energies

Supplementary materials



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