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 (Di) and Di/Dj 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. 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.