(278h) Large-Scale Computational Screening of Aluminosilicate Zeolites for Alkane Capture and Separation

Alkane separations are carried out industrially through energy intensive distillation processes. One potential non-thermal alternative to distillation is adsorption-based separation using porous materials. Zeolites are promising adsorbent materials that are widely used in the chemical process industries. However, large combinations of exchanged cations, framework topologies, and aluminum compositions are possible, making it challenging to identify the optimal candidates. Large scale computational studies present a more efficient strategy to identify the aforementioned candidates and design processes before further lab experimentation. These studies rely on accurate results from molecular simulation techniques such as Grand Canonical Monte Carlo (GCMC) or Molecular Dynamics (MD) and an efficient workflow. As such, many of these studies are limited by the accuracy and/or transferability of the available forcefields.

In recent work, our research group has developed a fully transferrable force field based on first-principles quantum mechanical (QM) methods that can accurately describe both the adsorption and diffusion properties of alkanes and some small adsorbates in siliceous and cationic zeolites. Moreover, we also assured ourselves that using rigid frameworks in screening projects was not only a time saving but also a valid assumption.

Following that, we developed a computational screening workflow for adsorption-based methane/butane separation in silica and Na/Ca exchanged cationic zeolites. We now present a machine learning tool for the screening of real and hypothetical aluminosilicate zeolites that can be used to facilitate high throughput screening for alkane separations and capture.