Inverse Design of ZIFs through Artificial Intelligence Methods

11 April 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Artificial Intelligence (AI) benefits research on membrane separations by facilitating fast and accurate performance predictions of a given material. However, the potential of AI to work backwards, towards predicting/designing a finetuned material for a given separation, remains untapped. Recent works report the inverse design of functionalized materials, such as metal-organic frameworks (MOFs), but they are limited to targeted sorption properties, while diffusivity, D, which is the driving force in membrane-based separations, is omitted. Herein, 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 (Di, Di/Dj) values of any given mixture of species i and j. We moreover display the efficacy of our tool, by designing ZIFs that meet industrial performance criteria of permeability and selectivity, for CO2/CH4, O2/N2 and C3H6/C3H8 mixtures. We validate the designed ZIFs through appropriate simulations, confirming the suitability of the AI-suggested ZIF designs.

Keywords

inverse design
separation membranes
metal-organic frameworks

Supplementary materials

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Supporting Information
Description
Includes information about the ZIFs of our database, our force fields, the simulations (TST and test-particle insertion), our Machine Learning models, and the implementation of the genetic algorithm.
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Supplementary weblinks

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