Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks

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.

Automated summary

Organic structure directing agents (OSDAs) play a crucial role in synthesizing micro- and mesoporous materials, especially zeolites, but their interaction mechanisms with zeolite frameworks are not well understood. A data-driven approach was used to establish OSDA-zeolite relationships through a comprehensive database of 5,663 synthesis routes. Structural features of OSDAs were analyzed using WHIM descriptors to relate them to different types of zeolites, and a generative neural network was adapted to suggest new OSDA candidates for specific zeolite structures and gel chemistry.