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 () and / of any given mixture of species and . We display the efficacy and validitiy of our tool, by designing ZIFs that meet industrial performance criteria of permeability and selectivity, for CO/CH, O/N and CH/CH mixtures.