Data-Driven Design of Biselective Templates for Intergrowth Zeolites

Area of Science:

• Materials Science
• Crystallography
• Computational Chemistry

Background:

• Zeolites are versatile inorganic materials with diverse industrial applications.
• Controlling zeolite crystallization, especially for intergrown frameworks, is challenging and often relies on empirical methods.
• Intergrown zeolite frameworks offer enhanced catalytic and transport properties by tailoring pore confinement.

Purpose of the Study:

• To design novel organic structure-directing agents (OSDAs) for the targeted synthesis of intergrown zeolites.
• To develop a computational framework for predicting OSDAs that favor specific zeolite intergrowth structures.
• To accelerate the discovery of new zeolite materials through data-driven synthesis optimization.

Main Methods:

• Utilizing computational simulations and data mining of a large zeolite-OSDA database.
• Proposing design principles for OSDAs selective towards end-member frameworks of disordered structures.
• Evaluating OSDAs based on phase competition metrics and framework shape selectivity.

Main Results:

• Successfully identified and downselected OSDA candidates for synthesizing known intergrowth zeolites (e.g., CHA/AFX, MTT/TON, BEC/ISV).
• Demonstrated that computationally designed OSDAs effectively balance phase competition and shape selectivity, bypassing the need for dual-OSDA systems.
• Proposed potential OSDAs for synthesizing hypothesized disordered zeolite frameworks (e.g., AEI/SAV).

Conclusions:

• Computational design of OSDAs offers a powerful strategy for controlling zeolite crystallization and synthesizing intergrown frameworks.
• This data-driven approach significantly accelerates zeolite discovery and materials design.
• The developed methodology provides a pathway for accessing novel zeolite structures with tailored properties.