Machine Learning-Aided Computational Study of Metal–Organic Frameworks for Sour Gas Sweetening

Nanoporous materials, such as metal–organic
frameworks (MOFs),
have shown great potential as adsorbents for separations in a wide
variety of energy- or environment-related applications. One promising
application is sour gas sweetening; a raw natural gas contains small
amounts of H2S that can be detrimental to the efficient
utilization of the energy source. However, the large database of nanoporous
materials has made the discovery of optimum materials significantly
demanding. While molecular simulations can play a complementary role
in facilitating the materials search, their brute-force utilization
still requires a vast amount of computational resources. In this study,
we incorporate a machine learning algorithm with structural and chemistry
descriptors as inputs for efficient screening. Specifically, the random
forest regressor, which can also be useful for elucidating structure–property
relationships, is employed. For reliable predictions with machine
learning, the choices of features play considerably important roles.
In addition to commonly adopted geometrical and chemical features,
we propose and incorporate a set of newly designed features for training
the model. These new features represent preferential binding sites
of open-metal sites and dense framework atoms on the pore surface.
Our analysis shows that the inclusion of the newly designed features
greatly improves the machine learning performance. Our work can pave
the way for the future design of nanoporous materials for sour gas
sweetening. These newly designed features can also be used for the
development of machine learning models for other applications, especially
those involving molecules with strong dipole and/or quadruple moments,
such as carbon capture.