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Updated: May 12, 2026

Synthesis and Characterization of Functionalized Metal-organic Frameworks
Published on: September 5, 2014
Inverse design of ZIFs through artificial intelligence methods
Panagiotis Krokidas1, Michael Kainourgiakis2, Theodore Steriotis3
1Institute of Informatics & Telecommunications, National Center for Scientific Research "Demokritos", 15341 Agia Paraskevi Attikis, Greece. p.krokidas@iit.demokritos.gr.
Abstract:
We report a tool combining a biologically inspired evolutionary algorithm with machine learning to design fine-tuned zeolitic-imidazolate frameworks (ZIFs), a sub-family of MOFs, for desired sets of diffusivities of species i (D) and D/D of any given mixture of species i and j. We display the efficacy and validitiy of our tool, by designing ZIFs that meet industrial performance criteria of permeability and selectivity, for CO2/CH4, O2/N2 and C3H6/C3H8 mixtures.
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