Inverse design of ZIFs through artificial intelligence methods

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_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 CO₂/CH₄, O₂/N₂ and C₃H₆/C₃H₈ mixtures.