posted on 2025-01-15, 08:29authored byI-Ting Sung, Ya-Hung Cheng, Chieh-Ming Hsieh, Li-Chiang Lin
This review addresses
a critical gap in the literature
by focusing
on the features (or descriptors) used in machine learning (ML) studies
to predict gaseous adsorption properties in metal–organic frameworks
(MOFs). Although ML approaches for predicting adsorption properties
in MOFs have been extensively reported in recent years, features employed
in ML models have not been thoroughly reviewed. A comprehensive review
of these features is crucial since they form the foundation for building
effective predictive models. These models are also key to facilitating
the inverse design of MOFs, as they can be used to efficiently predict
the performance of material candidates and explore the structure–property
relationship, guiding the creation of optimal MOF structures. Furthermore,
ML models can also be naturally employed in inverse design approaches,
such as encoder–decoder architectures. This review starts with
a brief overview of the importance and applications of MOFs in various
fields, followed by a discussion of the historical milestones of MOFs
in computational research, highlighting the critical role of ML. This
review then discusses traditional features and introduces newly proposed
distinctive features, referred to as “beyond traditional features”,
that have been reported to date. Furthermore, generalized ML models
for predicting the adsorption properties of different gases are also
outlined. Finally, we offer future outlooks on ML-assisted computational
searches for MOFs in adsorption applications. Overall, this review
aims to help researchers grasp current developments and offer insights
into future directions in this area.