Organic structure
directing agents (OSDAs) play a crucial role
in the synthesis of micro- and mesoporous materials especially in
the case of zeolites. Despite the wide use of OSDAs, their interaction
with zeolite frameworks is poorly understood, with researchers relying
on synthesis heuristics or computationally expensive techniques to
predict whether an organic molecule can act as an OSDA for a certain
zeolite. In this paper, we undertake a data-driven approach to unearth
generalized OSDA–zeolite relationships using a comprehensive
database comprising of 5,663 synthesis routes for porous materials.
To generate this comprehensive database, we use natural language processing
and text mining techniques to extract OSDAs, zeolite phases, and gel
chemistry from the scientific literature published between 1966 and
2020. Through structural featurization of the OSDAs using weighted
holistic invariant molecular (WHIM) descriptors, we relate OSDAs described
in the literature to different types of cage-based, small-pore zeolites.
Lastly, we adapt a generative neural network capable of suggesting
new molecules as potential OSDAs for a given zeolite structure and
gel chemistry. We apply this model to CHA and SFW zeolites generating
several alternative OSDA candidates to those currently used in practice.
These molecules are further vetted with molecular mechanics simulations
to show the model generates physically meaningful predictions. Our
model can automatically explore the OSDA space, reducing the amount
of simulation or experimentation needed to find new OSDA candidates.