| number | title | topic | team_leads | github | youtube_video | |
|---|---|---|---|---|---|---|
10 |
Navigating the black box of zeolite synthesis with Bayesian Optimization |
real-world |
|
AC-BO-Hackathon/project-zeolite-synthesis-bayesian-optimisation |
4lFEUixwkE8 |
Despite their massive industrial importance as catalysts, ion exchanger and adsorbent, zeolite synthesis still mostly relies on heuristics, experience and a sprinkle of magic. The parameter space is vast, comprising continuous variables (concentration of reagents, temperature, synthesis time...) and categorical variables (choice of precursor salts...). Different objectives are required depending on the specific target application (high synthesis yield, specific crystal size, target Si/Al, reducing expensive reagents, short synthesis time...). Employing Bayesian optimization has the potential to swiftly navigate the extensive parameter space in zeolite synthesis research and reduce associated costs compared to random or grid search.
References:
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Dusselier, M.; Davis, M. E. Small-Pore Zeolites: Synthesis and Catalysis. Chem. Rev. 2018, 118 (11), 5265– 5329, DOI: 10.1021/acs.chemrev.7b00738
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Mallette, A.J.; Shilpa, K; Rimer, J.D. The Current Understanding of Mechanistic Pathways in Zeolite Crystallization. Chem. Rev. 2024, DOI: 10.1021/acs.chemrev.3c00801