Computational Modeling and Screening of Crystalline Microporous Materials for Selective Separations and Catalysis

Abstract

Crystalline microporous materials, including zeolites and metal--organic frameworks (MOFs), are used widely for separations and catalysis due to their selectivity to molecular shape. While there are hundreds of known zeolites, thousands of MOFs, and millions of theoretically-plausible structures, only a few are commonly used in industry. To overcome the difficulties of exhaustively studying these materials experimentally, computational techniques are proposed to search in the materials genome and better understand their structures and properties. Effective screening methods are developed to identify candidate materials from large databases for further investigation.

An automated approach is presented for characterizing the three-dimensional pore structures of crystalline microporous materials. Geometry of the pores defines portals, channels, and cages, and topology of the connectivity network determines accessibility to molecules of different sizes. Quantities such as pore size distribution, accessible volume, and surface area are readily available. The three-dimensional pore network representations are used in a new method for calculating shape selectivity, which reflects the preference for a material to admit one type of molecule over another. Databases of zeolites and MOFs are screened to identify top candidates for several applications, including gas, air, and chemical separations.

Shape selectivity is incorporated into a multi-scale, hierarchical screening method for adsorption-based separation processes. Along with other novel metrics, including size and pore selectivities, databases of materials are narrowed down to a short list of candidates to be simulated and optimized in a realistic adsorption process model. New materials are discovered for carbon capture and natural gas purification that have the potential to dramatically reduce the cost of these separations.

New computational frameworks are designed for predicting reaction selectivity in zeolites that incorporate reactant, product, and transition state selectivities. Simultaneous reaction and diffusion systems are modeled for networks of interconnected reactions. A novel method is developed for automatically identifying chemical reaction mechanisms. Predictions for the toluene disproportionation reaction come into good agreement with experimental data, and preliminary results for a biomass application suggest the suitability of the approach to large-scale reaction networks.

A preliminary investigation into the modeling of modified zeolite structures aims to elucidate the positioning of aluminum atoms and extra-framework cations in zeolite NaY. These data could be used to expand the scope of the pore characterization and selectivity prediction methods toward a greater variety of relevant structures. The proposed methods offer vast possibilities for the discovery of new materials for industrially-significant applications, including hydrogen purification, xylene separation, and hydrocarbon catalysis.