Efficient Exploration
of Adsorption Space for Separations
in Metal–Organic Frameworks Combining the Use of Molecular
Simulations, Machine Learning, and Ideal Adsorbed Solution Theory
posted on 2023-09-14, 19:10authored byXiaohan Yu, Dai Tang, Jia Yuan Chng, David S. Sholl
Adsorption-based
separations using metal–organic
frameworks
(MOFs) are promising candidates for replacing common energy-intensive
separation processes. The so-called adsorption space formed by the
combination of billions of possible molecules and thousands of reported
MOFs is vast. It is very challenging to comprehensively evaluate the
performance of MOFs for chemical separation through experiments. Molecular
simulations and machine learning (ML) have been widely applied to
make predictions for adsorption-based separations. Previous ML approaches
to these issues were typically limited to smaller molecules and often
had poor accuracy in the dilute limit. To enable exploration of a
wider adsorption space, we carefully selected a diverse set of 45
molecules and 335 MOFs and generated single-component isotherms of
15,075 MOF–molecule pairs by grand canonical Monte Carlo. Using
this database, we successfully developed accurate (r2 > 0.9) machine learning models predicting adsorption
isotherms of diverse molecules in large libraries of MOFs. With this
approach, we can efficiently make predictions of large collections
of MOFs for arbitrary mixture separations. By combining molecular
simulation data and ML predictions with Ideal Adsorbed Solution Theory,
we tested the ability of these approaches to make predictions of adsorption
selectivity and loading for challenging near-azeotropic mixtures.