Inverse design of ZIFs through artificial intelligence methods

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 i ) and D i / D j of any given mixture of species i and j . We display...

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Bibliographic Details
Published in:Physical chemistry chemical physics : PCCP 2024-10, Vol.26 (39), p.25314-25318
Main Authors: Krokidas, Panagiotis, Kainourgiakis, Michael, Steriotis, Theodore, Giannakopoulos, George
Format: Article
Language:English
Subjects:
Artificial intelligence
Evolutionary algorithms
Inverse design
Machine learning
Metal-organic frameworks
Mixtures
Species diffusion
Zeolites
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Summary: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 i ) and D i / D j 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 CO 2 /CH 4 , O 2 /N 2 and C 3 H 6 /C 3 H 8 mixtures. We demonstrate an efficient inverse design scheme combining machine learning and genetic algorithms to design ZIFs with user-defined performance by assembling frameworks from building units, including metals, linkers, and functional groups.
ISSN:1463-9076
1463-9084
1463-9084
DOI:10.1039/d4cp02488e