Deeper Insight into the Rational Design and Synthesis of Zeolites Revealed by Machine Learning: A Mini Review

Authors

  • St Mardiana Division of Inorganic and Physical Chemistry, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesa no. 10, Bandung 40132,
  • Arxhel S. F. Nanda Division of Inorganic and Physical Chemistry, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesa no. 10, Bandung 40132,
  • I Made Arcana Division of Inorganic and Physical Chemistry, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesa no. 10, Bandung 40132,
  • Ismunandar Ismunandar Division of Inorganic and Physical Chemistry, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Jalan Ganesa no. 10, Bandung 40132,
  • Adroit T. N. Fajar Center for Energy System Design (CESD), International Institute for Carbon-Neutral Energy Research (WPI-I2CNER, Kyushu University, 744 Motooka, Fukuoka 819-0395
  • Grandprix T. M. Kadja Research Center for Nanosciences and Nanotechnology, Institut Teknologi Bandung, Jalan Ganesa no. 10, Bandung 40132

Abstract

Zeolites are widely applied in various fields owing to their outstanding properties. However, our understanding on the nature of zeolite synthesis is not completed yet due to its high dimensional parameters. Machine learning has the ability to unravel fundamental relationships between complex parameters and predict the possible outcomes; thus, it can potentially reveal the nature of zeolite synthesis. This mini review highlights the current use of machine learning to comprehend the black box issue in zeolite synthesis. Conventional syntheses of zeolite were also elaborated to showcase the gap between traditional methods and machine learning approaches in zeolite synthesis. The future prospects of machine learning applications in zeolite synthesis are also discussed. This mini-review may bring crucial insights on the zeolite synthesis process.

How to Cite

St Mardiana, Arxhel S. F. Nanda, I Made Arcana, Ismunandar Ismunandar, Adroit T. N. Fajar, Grandprix T. M. Kadja (2025). Deeper Insight into the Rational Design and Synthesis of Zeolites Revealed by Machine Learning: A Mini Review. Journal of Engineering and Technological Sciences, Vol. 57 No. 4 (2025): Vol. 57 No. 4 (2025): August , 475-491. https://doi.org/10.5614/j.eng.technol.sci.2025.57.4.4

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Date Log

Submitted
September 10, 2024
Accepted
July 3, 2025
Published
July 25, 2025

Keywords

  • black-box
  • machine learning
  • synthesis
  • zeolite
  • zeolite prediction

How to Cite

Deeper Insight into the Rational Design and Synthesis of Zeolites Revealed by Machine Learning: A Mini Review. (2025). Journal of Engineering and Technological Sciences, 57(4), 475-491. https://doi.org/10.5614/j.eng.technol.sci.2025.57.4.4