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Efficiently Exploring Adsorption Space to Identify Privileged Adsorbents for Chemical Separations of a Diverse Set of Molecules
Author(s) -
Tang Dai,
Wu Ying,
Verploegh Ross J.,
Sholl David S.
Publication year - 2018
Publication title -
chemsuschem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.412
H-Index - 157
eISSN - 1864-564X
pISSN - 1864-5631
DOI - 10.1002/cssc.201702289
Subject(s) - adsorption , molecule , chemical space , polymer , chemistry , solubility , molecular descriptor , chemical physics , computational chemistry , nanotechnology , materials science , organic chemistry , quantitative structure–activity relationship , stereochemistry , drug discovery , biochemistry
Although computational models have been used to predict adsorption of molecules in large libraries of porous adsorbents, previous work of this kind has focused on a small number of molecules as potential adsorbates. In this study, molecular simulations were used to consider the adsorption of a diverse range of molecules in a large collection of metal–organic framework (MOF) materials. Specifically, 11 304 isotherms were obtained from molecular simulations of 24 different adsorbates in 471 MOFs. This information provides insight into several interesting questions that could not be addressed with previously available data. Highly computationally efficient methods are introduced that can predict isotherms for a wide range of adsorbing molecules with far less computation than traditional molecular simulations. By characterizing the 276 binary mixtures defined by the molecules considered, “privileged” adsorbents are shown to exist, which are effective for separating many different molecular mixtures. Finally, correlations that were developed previously to predict molecular solubility in polymers are found to be surprisingly effective in predicting the average properties of molecules adsorbing in MOFs.