Prediction of water stability of metal–organic frameworks using machine learning

Abstract

Owing to their highly tunable structures, metal–organic frameworks (MOFs) are considered suitable candidates for a range of applications, including adsorption, separation, sensing and catalysis. However, MOFs must be stable in water vapour to be considered industrially viable. It is currently challenging to predict water stability in MOFs; experiments involve time-intensive MOF synthesis, while modelling techniques do not reliably capture the water stability behaviour. Here, we build a machine learning-based model to accurately and instantly classify MOFs as stable or unstable depending on the target application, or the amount of water exposed. The model is trained using an empirically measured dataset of water stabilities for over 200 MOFs, and uses a comprehensive set of chemical features capturing information about their constituent metal node, organic ligand and metal–ligand molar ratios. In addition to screening stable MOF candidates for future experiments, the trained models were used to extract a number of simple water stability trends in MOFs. This approach is general and can also be used to screen MOFs for other design criteria. Metal–organic frameworks (MOFs) are attractive materials for gas capture, separation, sensing and catalysis. Determining their water stability is important, but time-intensive. Batra et al. use machine learning to screen water-stable MOFs and identify chemical features supporting their stability.