Improving Computational Assessment of Porous Materials for Water Adsorption Applications via Flat Histogram Methods

Abstract

Water adsorption in porous materials has recently drawn considerable attention for its tremendous potential in environmental applications such as water harvesting from air. A key step to the deployment of such technologies is the selection of optimal adsorbent materials as material performance directly impacts their efficiency. Owing to the vast materials space, computational studies may play a critically important role in facilitating the selection of the best material. The current state-of-the-art method to predict adsorption behavior of porous materials, the grand canonical Monte Carlo (GCMC) simulations, however, can converge very slowly due to its inefficient sampling of the phase space accessible to the system and may yield unreliable results. To this end, we have demonstrated a method from a class of techniques known as flat histogram methods, which can sample the accessible states of the system much more efficiently. Further, a so-called C-map method is proposed herein to efficiently determine the applicability of a material in water adsorption applications. These methods can offer unprecedented insights into the behaviors of the GCMC-predicted isotherms and, more importantly, are promising for large-scale computational screening of porous materials for water adsorption.