Artificial Intelligence Predicted OSDAs Enable Direct Synthesis of Interlayer-Expanded Zeolites

Zeolite crystallization is a metastable process under
harsh conditions
with poorly understood mechanisms, making the directed synthesis of
specific frameworks challenging. Organic structure-directing agents
(OSDAs) are key to framework control, but their discovery remains
dominated by trial-and-error screening. Here, we develop a domain
knowledge-informed machine learning model to predict OSDAs, which
enables the successful synthesis of three novel zeolites, namely,
ECNU-30, ECNU-34, and ECNU-40 (named after East China Normal University),
validating the efficacy of the model. Traditional descriptor-based
machine learning models exhibit limited predictive performance in
screening OSDAs for unknown zeolite frameworks. Combining an end-to-end
architecture with active learning, the ECNU-Zeoformer effectively
overcomes this limitation, enabling more accurate prediction of OSDA-zeolite
binding energies for selecting suitable OSDAs and superior generalizability
to different framework topologies.