Exploring Hydrogen Storage Capacity in Metal-Organic Frameworks: A Bayesian Optimization Approach

Metal-organic Frameworks (MOFs) can be employed for gas storage, capture, and sensing. Finding the MOF with the best adsorption property from a large database is usual for adsorption calculations. In high-throughput computational research, the expense of computing thermodynamic quantities limits the finding of MOFs for separations and storage. In this work, we demonstrate the usefulness of Bayesian optimization (BO) for estimating the H2 uptake capability of MOFs by using an existing dataset containing 98000 real and hypothetical MOFs. We demonstrate that in order to recover the best candidate MOFs, less than 0.027 % of the database needs to be screened using the BO method. This allows future adsorption experiments on a small sample of MOFs to be undertaken with minimal experimental effort by effectively screening MOF databases. In addition, the presented BO can provide comprehensible material design insights, and the framework will be transferable to optimizing other target properties. We also suggest using Particle Swarm Optimisation (PSO), a swarm intelligence technique in artificial intelligence, to estimate MOFs' H2 uptake potential to achieve results comparable to BO. In addition, we implement a novel modification of PSO called Evolutionary-PSO (EPSO) to compare and find interesting outcomes. In this article, we show how Bayesian Optimisation (BO) may be used to find the MOF with the highest simulated hydrogen storage capacity. Furthermore, we demonstrate that BO searches more effectively than evolutionary and one-shot supervised machine learning approaches.image