Efficient Discovery of Air Separation Adsorbents via Multi-Fidelity Bayesian Optimization

Metal-organic frameworks provide seemingly endless choices of metal and organic species to tune small molecule adsorption behavior. Specifically, we examine MFU-4l and its variants as a platform for O2/N2 gas separation, an application where solid adsorbents promise to dramatically reduce the energy intensity of a traditionally costly and inefficient process. We enumerate a set of possible structure modifications of MFU-4l as combinations of metal species and organic ligands to develop a list of over 10,000 structures, and we screen their binding affinity for O2 and N2 using ab initio density functional theory calculations. To reduce computational effort, we use a Bayesian optimization approach with a multi-fidelity surrogate model that combines both ab initio and experimentally-obtained binding energy data. Through this work, we present candidate materials that can selectively separate O2 from N2 at ambient conditions with dramatically decreased energy cost relative to current adsorbents.