Inverse Design of ZIFs through Artificial Intelligence Methods

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.

Inverse design of porous materials: a diffusion model approach

“…Specically, pure silica zeolites that consist of silicon and oxygen atoms were targeted for the generation. Among the previous attempts made with regard to the design of zeolites and other porous materials, [19][20][21][22][23][24][25][26][27][28][29][30][31] it must be stated that Deem and coworkers 32,33 have employed the Monte Carlo approach, and Kim et al 34 used the GAN architecture to generate new zeolite structures. However, Deem's approach lacked the ability to generate structures with user-desired properties, while the structures generated using Kim's GAN architecture exhibited poor structural validity.…”