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

Mardiana, St
S. F. Nanda, Arxhel
Arcana, I Made
Ismunandar, Ismunandar
Fajar, Adroit T. N.
Kadja, Grandprix T. M.

Journal of Engineering and Technological Sciences
Vol. 57 No. 4 (2025): Vol. 57 No. 4 (2025): August

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.