Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks.
Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks.
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DOI：
10.1021/acscentsci.1c00024
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发表时间：
2021-05-26
影响因子：
18.2
通讯作者：
Olivetti EA
中科院分区：
文献类型：
--
作者：
Jensen Z;Kwon S;Schwalbe-Koda D;Paris C;Gómez-Bombarelli R;Román-Leshkov Y;Corma A;Moliner M;Olivetti EA
有机结构导向剂（OSDA）在微孔和介孔材料的合成中起着至关重要的作用，特别是在沸石的情况下。尽管OSDA被广泛使用，但它们与沸石骨架的相互作用却知之甚少，研究人员依赖合成化学或计算昂贵的技术来预测有机分子是否可以作为某种沸石的OSDA。在本文中，我们采用数据驱动的方法，使用包含5，663种多孔材料合成路线的综合数据库来挖掘广义OSDA-沸石关系。为了生成这个全面的数据库，我们使用自然语言处理和文本挖掘技术从1966年至2020年出版的科学文献中提取OSDA，沸石相和凝胶化学。通过使用加权整体不变分子（WHIM）描述符的OSDAs的结构特征化，我们与文献中描述的OSDAs不同类型的笼型，小孔沸石。最后，我们采用了一个生成神经网络，能够建议新的分子作为潜在的OSDA为给定的沸石结构和凝胶化学。我们将该模型应用于CHA和SFW沸石，产生几种替代OSDA候选物，以替代目前在实践中使用的那些。这些分子进一步通过分子力学模拟进行审查，以显示该模型产生了物理上有意义的预测。我们的模型可以自动探索OSDA空间，减少寻找新的OSDA候选者所需的模拟或实验量。我们使用WHIM特征化和生成式建模从通过自然语言处理自动提取的文献数据中建模OSDA和沸石之间的相互作用。
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. We model the interactions between OSDAs and zeolites using WHIM featurization and generative modeling from literature data extracted automatically through natural language processing.
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影响因子：
64.8
作者：
Corma, A;Rey, F;Valencia, S
通讯作者：
Valencia, S
影响因子：
18.2
作者：
Gómez-Bombarelli R;Wei JN;Duvenaud D;Hernández-Lobato JM;Sánchez-Lengeling B;Sheberla D;Aguilera-Iparraguirre J;Hirzel TD;Adams RP;Aspuru-Guzik A
通讯作者：
Aspuru-Guzik A
影响因子：
8.6
作者：
Kim, Edward;Huang, Kevin;Olivetti, Elsa
通讯作者：
Olivetti, Elsa
影响因子：
8.6
作者：
Davis, Tracy M.;Liu, Albert Tianxiang;Deem, Michael W.
通讯作者：
Deem, Michael W.
影响因子：
8.6
作者：
Boal, Ben W.;Schmidt, Joel E.;Davis, Mark E.
通讯作者：
Davis, Mark E.