Zeolites, nanoporous
aluminosilicates with well-defined porous
structures, are versatile materials with applications in catalysis,
gas separation, and ion exchange. Hydrothermal synthesis is widely
used for zeolite production, offering control over composition, crystallinity,
and pore size. However, the intricate interplay of synthesis parameters
necessitates a comprehensive understanding of synthesis–structure
relationships to optimize the synthesis process. Hitherto, public
zeolite synthesis databases only contain a subset of parameters and
are small in scale, comprising up to a few thousand synthesis routes.
We present ZeoSyn, a dataset of 23,961 zeolite hydrothermal synthesis
routes, encompassing 233 zeolite topologies and 921 organic structure-directing
agents (OSDAs). Each synthesis route comprises comprehensive synthesis
parameters: 1) gel composition, 2) reaction conditions, 3) OSDAs,
and 4) zeolite products. Using ZeoSyn, we develop a machine learning
classifier to predict the resultant zeolite given a synthesis route
with >70% accuracy. We employ SHapley Additive exPlanations (SHAP)
to uncover key synthesis parameters for >200 zeolite frameworks.
We
introduce an aggregation approach to extend SHAP to all building units.
We demonstrate applications of this approach to phase-selective and
intergrowth synthesis. This comprehensive analysis illuminates the
synthesis parameters pivotal in driving zeolite crystallization, offering
the potential to guide the synthesis of desired zeolites. The dataset
is available at .