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

Abstract

Ammonia (NH3) has been a subject of great interest due to its important roles in diverse technological applications. However, high toxicity and corrosiveness of NH3 has made it an important task to develop an efficient carrier to safely capture NH3 with high capacity. Here, we employ a machine learning (ML) model to discover high-performance metal organic frameworks (MOFs) that will work as efficient NH3 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 NH3 carriers through screening the large-scale MOF databases with the trained models and GCMC verification. The identified MOFs exhibit the average NH3 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.

Keywords

• Metal organic framework
• Grand canonical Monte Carlo
• Machine learning
• Ammonia capture