High-efficiency prediction of water adsorption performance of porous adsorbents by lattice grand canonical Monte Carlo molecular simulation

Abstract

Water adsorption has come under the spotlight for its tremendous potential in numerous environment- and energy-related applications. Given the vast adsorbent space, computational studies play a critically significant role in facilitating the discovery of potential candidates. However, large-scale computational deployment by conventional grand canonical Monte Carlo (GCMC) to identify optimal water adsorbents is challenging due to its extreme computation time and expense. In this work, a lattice GCMC method (LGCMC) with hierarchically constructed discretized interaction of host–guest and guest–guest driven by atomistic potentials was attempted to accurately and rapidly simulate the water adsorption performance of adsorbents using a coarse-grained Molinero water (mW) model. Nevertheless, given the monatomic nature of the mW model, leading to different phase behaviors in nanoscale confinement, a remarkable discrepancy in the primitive LGCMC-predicted isotherms, especially different step positions, compared with experiments was observed. Thus, a general correction strategy of water adsorption isotherm by tuning the saturation pressure was adopted. Taking metal–organic frameworks (MOFs) as examples, simulated water adsorption isotherms consistent with experimental results were obtained by the correction strategy using LGCMC. It is worth highlighting that the simulation of water adsorption in adsorbents by LGCMC can be accomplished within a few hours, which yields a significant acceleration of two to three orders of magnitude compared to conventional GCMC simulations. Therefore, the corrected LGCMC is a powerful tool to screen a huge number of adsorbents to facilitate the discovery of potential adsorbents for water adsorption-related applications, and this study provides microscopic insights into water adsorption mechanisms in porous adsorbents.