Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks
Name
acscentsci.1c00024.pdf
Description
Published version
Size
2.74 MB
Format
Adobe PDF
Checksum (MD5)
61eac63baa69d949ab66573d8ba4a18c
Author(s)
Jensen, Zach
Kwon, Soonhyoung
Schwalbe-Koda, Daniel
Paris, Cecilia
Gómez-Bombarelli, Rafael
Román-Leshkov, Yuriy
Corma, Avelino
Moliner, Manuel
Olivetti, Elsa A
Date Issued
2021
Journal
ACS Central Science
Publisher
American Chemical Society (ACS)
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1021/acscentsci.1c00024
