Metal–Organic Frameworks for Xylene Separation: From Computational Screening to Machine Learning

Separation
of xylene isomers is an important process in the chemical
industry and there has been considerable interest in developing advanced
materials for xylene separation. In this study, we synergize computational
screening and machine learning to explore the selective adsorption
of p-xylene over o- and m-xylene in metal–organic frameworks (MOFs). First,
a large set (4764) of computation-ready experimental MOFs is screened
by geometric analysis and molecular simulation. The relationships
between MOF structural descriptors (void fraction, volumetric surface
area, and largest cavity diameter) and separation performance metrics
(adsorption capacity of p-xylene Np‑xylene and selectivity
of p-xylene over o- and m-xylene Sp/(m+o)) are established.
Then two machine-learning methods (back-propagation neural network
and decision tree), as well as particle swarm optimization, are utilized
to analyze and optimize Np‑xylene and Sp/(m+o). The importance
of each descriptor for separation is evaluated in six different MOF
data sets. In the 100 top-performing MOFs, the pore limiting diameter
(PLD) and largest cavity diameter (LCD) are revealed to be key factors
governing separation performance. On the basis of the threshold values
of Np‑xylene > 0.5 mol/kg and Sp/(m+o) > 5, seven
top-performing
MOFs are identified. By further incorporating framework flexibility,
JIVFUQ is predicted to be the best and superior to many reported MOFs
in the literature.