Integrating Molecular Simulations with Machine Learning Guides in the Design and Synthesis of [BMIM][BF₄]/MOF Composites for CO₂/N₂ Separation.

ABSTRACT: Considering the existence of a large number and variety of metal-organic frameworks (MOFs) and ionic liquids (ILs), assessing the gas separation potential of all possible IL/MOF composites by purely experimental methods is not practical. In this work, we combined molecular simulations and machine learning (ML) algorithms to computationally design an IL/MOF composite. Molecular simulations were first performed to screen approximately 1000 different composites of 1-n-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF₄]) with a large variety of MOFs for CO₂ and N₂ adsorption. The results of simulations were used to develop ML models that can accurately predict the adsorption and separation performances of [BMIM][BF₄]/MOF composites. The m

SUBMITTER: Daglar H 

PROVIDER: S-EPMC10080536 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature