Computing Mixture Adsorption in Porous Materials through Flat Histogram Monte Carlo Methods

Abstract

Mixture adsorption properties of porous materials are critical to determine their potential as adsorbents in separation applications. Toward the discovery of optimal adsorbents, in silico screening studies typically employ the grand canonical Monte Carlo (GCMC) technique to compute adsorption properties of gas mixtures in materials of interest at a given condition (i.e., composition, total pressure, and temperature) or to compute their adsorption properties for each component, followed by utilizing methods to predict mixture adsorption isotherms. However, the former approach results in the need for repeated calculations when different conditions such as compositions are considered. For the latter, the predictions may involve uncertainties, sometimes originating from the fitting quality to the pure component isotherms, and repeated simulations may also be needed for different temperatures. To this end, this study demonstrates the potential of flat histogram Monte Carlo methods in addressing the abovementioned shortfalls. Specifically, the so-called NVT + W method, first reported by Smit and co-workers, is extended herein to determine the macrostate probability distribution (MPD) of binary mixtures in porous materials. The obtained MPD can be reweighted to any conditions, yielding accurate adsorption isotherms of any desired compositions and temperatures. This approach, denoted as 2D NVT + W, is also compared with the widely adopted ideal adsorbed solution theory (IAST) method, and the former is found to offer more reliable predictions. Overall, the 2D NVT + W approach represents an efficient and effective alternative to compute mixture adsorption isotherms for porous materials, and the obtained MPD can be conveniently reused by peer researchers. A user-friendly Python code is also provided along with this article to employ this method.

Quantitative Structure–Activity Relationship Models for the Angiotensin-Converting Enzyme Inhibitory Activities of Short-Chain Peptides of Goat Milk Using Quasi-SMILES

“…An unexpected obstacle on this path is the contradiction between the mathematical aiming of accuracy or ideality, on the one hand, and the aiming of instability and dispersion of many natural phenomena, including the values of endpoints used for the characterization and evaluation of substances for various purposes. Monte Carlo methods [11][12][13][14][15][16][17][18][19][20], in principle, can be a compromise in resolving the aforementioned contradiction between nature and mathematics. However, the cost of this compromise is uncertain; consequently, one can discuss some intervals as a more useful indicator compared with pseudo-reliable and pseudo-accurate theoretical values for physicochemical and biochemical endpoints.…”