ArticleACS central science2021
Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks.
Article in ACS central science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
16 citing papers in PubMed.
- Sustainable zeolite synthesis: advances in green strategies and AI-assisted design.Frontiers in chemistry · 2026Review
- Single Crystals of Vanadium Oxides as a Lens for Understanding Structural and Electronic Phase Transformations, Ion Transport, Chemo-Mechanical Coupling, and Electrothermal Neuronal Emulation.Chemical reviews · 2025Review
- Dual organic structure-directing agents in zeolite synthesis: cooperation or competition?Materials horizons · 2025Review
- Strength of London Dispersion Forces in Organic Structure Directing Agent-Zeolite Assemblies.Molecules (Basel, Switzerland) · 2024Article
- ZeoSyn: A Comprehensive Zeolite Synthesis Dataset Enabling Machine-Learning Rationalization of Hydrothermal Parameters.ACS central science · 2024Article
- Full-scale modeling of chemical experiments.Smart molecules : open access · 2024Article
- Competition between Mononuclear and Binuclear Copper Sites across Different Zeolite Topologies.JACS Au · 2024Article
- Latent Variable Machine Learning Framework for Catalysis: General Models, Transfer Learning, and Interpretability.JACS Au · 2024Article
- A Comprehensive and Versatile Multimodal Deep-Learning Approach for Predicting Diverse Properties of Advanced Materials.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Machine learning-assisted crystal engineering of a zeolite.Nature communications · 2023Article
- Approaching enzymatic catalysis with zeolites or how to select one reaction mechanism competing with others.Nature communications · 2023Article
- Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning.Angewandte Chemie (International ed. in English) · 2022Article
- MOFSimplify, machine learning models with extracted stability data of three thousand metal-organic frameworks.Scientific data · 2022Article
- Analysis of Data Interaction Process Based on Data Mining and Neural Network Topology Visualization.Computational intelligence and neuroscience · 2022Article
- Enterprise Information Security Management Using Internet of Things Combined with Artificial Intelligence Technology.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.