Efficient Models for Predicting Temperature-Dependent Henry’s Constants and Adsorption Selectivities for Diverse Collections of Molecules in Metal–Organic Frameworks

Adsorption-based
separations using metal–organic frameworks
(MOFs) are a promising alternative to traditional energy-intensive
separation process. Machine learning (ML) methods have been applied
to predict large collections of adsorption isotherms in MOFs. Previous
ML models, however, focus only on predicting single-component adsorption
isotherms of a small number of molecules at a single temperature and
lack accuracy in the dilute limit. Here we describe a useful strategy
for predicting Henry’s constants and heats of adsorption for
a diverse set of molecules in large collections of MOFs. To achieve
this, a data set containing 21,195 MOF–molecule pairs with
45 adsorbates in 471 MOFs is generated, and a set of 135 descriptors
combining energy and chemical information is developed. Robust ML
models are developed to predict Henry’s constants and heats
of adsorption after removing physically unfavorable adsorption pairs.
The adsorption selectivity of near-azeotropic mixtures at two temperatures
(300 and 373 K) is predicted with acceptable accuracy by using the
predicted Henry’s constants and heats of adsorption. The ability
to make temperature-dependent predictions is important for many practical
separation applications. Our work sheds light on important challenges
and opportunities for developing accurate models predicting adsorption
properties for diverse collection of adsorbates and adsorbents.