Data-driven discovery of novel metal organic frameworks with superior ammonia adsorption capacity

Ammonia (NH 3 ) has been a subject of great interest due to its important roles in diverse technological applications. However, high toxicity and corrosiveness of NH 3 has made it an important task to develop an efficient carrier to safely capture NH 3 with high capacity. Here, we employ a machine learning (ML) model to discover high-performance metal organic frameworks (MOFs) that will work as efficient NH 3 carriers. By constructing databases at two distinct conditions, adsorption and desorption, through Grand Canonical Monte Carlo (GCMC) simulations to train ML models, we identify eight novel MOFs as potentially efficient NH 3 carriers through screening the large-scale MOF databases with the trained models and GCMC verification. The identified MOFs exhibit the average NH 3 working capacity exceeding 1100 mg/g, and subsequent molecular dynamics simulations demonstrate mechanical stability of the predicted MOFs. Moreover, analyses of the diffusion mechanism within the proposed MOFs underscore the strong dependence of NH₃ gas diffusivity on the structural details of the materials.