Catalysis

Data-driven design of bi-selective OSDAs for intergrowth zeolites

Authors

  • Daniel Schwalbe-Koda Massachusetts Institute of Technology ,
  • Avelino Corma Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas ,
  • Yuriy Román-Leshkov Massachusetts Institute of Technology ,
  • Manuel Moliner Instituto de Tecnología Química, Universitat Politècnica de València-Consejo Superior de Investigaciones Científicas ,
  • Rafael Gómez-Bombarelli Massachusetts Institute of Technology

Abstract

Zeolites are inorganic materials with wide industrial applications due to their topological diversity. Tailoring confinement effects in zeolite pores, for instance by crystallizing intergrown frameworks, can improve their catalytic and transport properties, but controlling zeolite crystallization often relies on heuristics. In this work, we use computational simulations and data mining to design organic structure-directing agents (OSDAs) to favor the synthesis of intergrown zeolites. First, we propose design principles to identify OSDAs which are selective towards both end members of the disordered structure. Then, we mine a database of hundreds of thousands of zeolite-OSDA pairs and downselect OSDA candidates to synthesize known intergrowth zeolites such as CHA/AFX, MTT/TON, and BEC/ISV. The computationally designed OSDAs balance phase competition metrics and shape selectivity towards the frameworks, thus bypassing expensive dual-OSDA approaches typically used in the synthesis of intergrowths. Finally, we propose potential OSDAs to obtain hypothesized disordered frameworks such as AEI/SAV. This work may accelerate zeolite discovery through data-driven synthesis optimization and design.

Content

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Supplementary material

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Supporting Information for: Data-driven design of bi-selective OSDAs for intergrowth zeolites
Supporting information for the manuscript.