Accelerated Discovery of CH4 Uptake Capacity MOFs using Bayesian Optimization

High-throughput computational studies for discovery of metal-organic frameworks (MOFs) for separations and storage applications are often limited by the costs of computing thermodynamic quantities, with recent studies reliant ab initio results for a narrow selection of MOFs and empirical force-field methods for larger selections. Here, we conduct a proof-of-concept study using Bayesian optimization on CH4 uptake capacity of hypothetical MOFs for an existing dataset (Wilmer et al, Nature Chem. 2012, 4, 83). We show that less than 0.1% of the database needs to be screened with our Bayesian optimization approach to recover the top candidate MOFs. This opens the possibility of efficient screening of MOF databases using accurate ab-initio calculations for future adsorption studies on a minimal subset of MOFs. Furthermore, Bayesian optimization and the surrogate model presented here can offer interpretable material design insights and our framework will be applicable in the context of other target properties.