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Area of Science:

  • Materials Science
  • Crystallography
  • Computational Chemistry

Background:

  • Predicting and directing polymorphic transformations in zeolite synthesis presents a significant challenge.
  • Interzeolite transformations offer selective crystallization but are complex to design using traditional crystal structure comparisons.

Purpose of the Study:

  • To computationally mine literature data on polymorphic transformations and analyze interzeolite relationships.
  • To develop a theoretical framework for understanding and predicting zeolite polymorphism and intergrowth.

Main Methods:

  • Utilized computational and theoretical tools for exhaustive data mining of reported zeolite transformations.
  • Introduced a supercell-invariant metric based on graph theory to compare crystal structures.
  • Analyzed relationships between structural similarity and graph similarity in zeolite frameworks.

Main Results:

  • Crystallographic building units are inadequate predictors of topology interconversion and intergrowth.
  • Diffusionless transformations occur exclusively between graph-similar zeolite pairs.
  • Intergrowth events are observed between structurally similar or graph-similar frameworks.
  • Identified numerous potential pairs for diffusionless transformations and intergrowth among known and hypothetical zeolites.

Conclusions:

  • A graph theory-based approach provides a robust method for understanding zeolite polymorphism.
  • The developed theory can guide the rational design and synthesis of novel zeolite materials.
  • This work offers a pathway to control and predict zeolite transformations, advancing materials discovery.