Integrating stability metrics with high-throughput computational screening of metal–organic frameworks for CO2 capture

Abstract

Metal–organic frameworks (MOFs) have been considered a unique class of hybrid materials for a wide variety of potential applications. With the existence of almost infinite MOFs, high-throughput computational screening (HTCS) is a robust technique to accelerate the search for promising MOFs. However, conventional HTCS studies reported in the literature neglect the stability of MOFs, which must be considered for practical applications. Here we integrate four stability metrics (thermodynamic, mechanical, thermal, and activation) with HTCS to identify top-performing, synthesizable, and stable hypothetical MOFs for CO2 capture. The thermodynamic and mechanical stabilities are evaluated through molecular dynamics simulations, while the activation and thermal stabilities are predicted using machine learning models. Finally, we identify top-performing hypothetical MOFs satisfying all these stability metrics. This study underlines the central importance of integrating stability metrics when screening MOFs for applications.