(582i) Deep Learning Combined with IAST to Screen Thermodynamically Feasible MOFs for Adsorption-Based Separation of Multiple Binary Mixtures

The structures of metal-organic frameworks (MOFs) can be tuned to reproducibly create adsorption properties that enable the use of these materials in fixed-adsorption beds for non-thermal separations. However, with millions of possible MOF structures, the challenge is to find the MOF with the best adsorption properties to separate a given mixture. Thus, computational, rather than experimental, screening is necessary to identify promising MOF structures that merit further examination, a process traditionally done using molecular simulation. However, even molecular simulation can become intractable when screening an expansive MOF database for their separation properties at more than a few composition, temperature, and pressure combinations. Here, we illustrate progress towards an alternative computational framework that can efficiently identify the highest-performing MOFs for separating various gas mixtures at a variety of conditions and at a fraction of the computational cost of molecular simulation. This framework uses a “multipurpose” multilayer perceptron (MLP) model that can predict single component adsorption of various small adsorbates which, upon coupling with ideal adsorbed solution theory (IAST), can predict binary adsorption for mixtures such as Xe/Kr, CH4/CH6, N2/CH4 and Ar/Kr at multiple compositions and pressures.

For this MLP+IAST framework to work with sufficient accuracy we found it critical for the MLP to make accurate predictions at low pressures (0.01-0.1 bar). After training a model with this capability, we found that MOFs in the 95th and 90th percentiles of separation performance determined from MLP+IAST calculations were 65% and 87%, respectively, the same as MOFs in the simulation-predicted 95th percentile across several mixtures at diverse conditions (on average). After validating our MLP+IAST framework, we used a clustering algorithm to identify “privileged” MOFs that are high performing for multiple separations at multiple conditions. As an example, we focused on MOFs that were high performing for the industrially relevant separations Xe/Kr at 1 bar and N2/CH4 at 5 bar. Finally, we used the MOF free energies (calculated on our entire database) to identify privileged MOFs that were also likely synthetically accessible, at least from a thermodynamically perspective.

References:

1. Anderson, Ryther; Gómez-Gualdrón, Diego (2021): Deep Learning Combined with IAST to Screen Thermodynamically Feasible MOFs for Adsorption-Based Separation of Multiple Binary Mixtures. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.14122901.v1