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.
Supplementary materials
Title
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
Title
Inverse design code
Description
Gives access to our code, with the GA implementation for the design of the three ZIFs presented in the manuscript
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