ArticleNature communications2023
Machine learning-assisted crystal engineering of a zeolite.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 46 citations in OpenAlex.
- Electro-assisted control over zeolite crystallization and modification: theory, configuration, and applications.Journal of materials chemistry. A · 2026Review
- Dual-Route Accessible Zeolites: bridging organic- and inorganic-directed syntheses toward greener zeolite production.Chemical science · 2026Review
- Recent Synthesis Routes to Previously Inaccessible Zeolite Porosity: From Ultrasmall Pore to Ultraporous Materials.ACS applied materials & interfaces · 2026Review
- Review
- Harnessing confinement effect and interpretable machine learning to predict alkane diffusion in zeolite catalysts.Nature communications · 2026Article
- Encapsulated Non-Exchangeable NaAngewandte Chemie (International ed. in English) · 2026Article
- Sustainable zeolite synthesis: advances in green strategies and AI-assisted design.Frontiers in chemistry · 2026Review
- Article
- Application of Synchrotron Radiation Based X-Ray Diffraction in Zeolite Research: Advanced Analysis from Atomic Structure to Dynamic Behavior.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Zeolite membrane with sub-nanofluidic channels for superior blue energy harvesting.Nature communications · 2024Article
- Article
- Machine learning insights into predicting biogas separation in metal-organic frameworks.Communications chemistry · 2024Article
- The Synthesis of Well-Dispersed and Uniform-Sized Zeolite NaY by Adding Non-Refluxed and Acid-Refluxed Cogon Grass.Materials (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
13 authors at 5 institutions in 2 countries.
Funding
Abstract
It is shown that Machine Learning (ML) algorithms can usefully capture the effect of crystallization composition and conditions (inputs) on key microstructural characteristics (outputs) of faujasite type zeolites (structure types FAU, EMT, and their intergrowths), which are widely used zeolite catalysts and adsorbents. The utility of ML (in particular, Geometric Harmonics) toward learning input-output relationships of interest is demonstrated, and a comparison with Neural Networks and Gaussian Process Regression, as alternative approaches, is provided. Through ML, synthesis conditions were identified to enhance the Si/Al ratio of high purity FAU zeolite to the hitherto highest level (i.e., Si/Al = 3.5) achieved via direct (not seeded), and organic structure-directing-agent-free synthesis from sodium aluminosilicate sols. The analysis of the ML algorithms' results offers the insight that reduced Na
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What OpenQuestion holds
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.