A Robust Machine Learning Algorithm for the Prediction of Methane Adsorption in Nanoporous Materials

In the present study,
we propose a new set of descriptors that, along with a few structural
features of nanoporous materials, can be used by machine learning
algorithms for accurate predictions of the gas uptake capacities of
these materials. All new descriptors closely resemble the helium atom
void fraction of the material framework. However, instead of a helium
atom, a particle with an appropriately defined van der Waals radius
is used. The set of void fractions of a small number of these particles
is found to be sufficient to characterize uniquely the structure of
each material and to account for the most important topological features.
We assess the accuracy of our approach by examining the predictions
of the random forest algorithm in the relative small dataset of the
computation-ready, experimental (CoRE) MOFs (∼4700 structures)
that have been experimentally synthesized and whose geometrical/structural
features have been accurately calculated before. We first performed
grand canonical Monte Carlo simulations to accurately determine their
methane uptake capacities at two different temperatures (280 and 298
K) and three different pressures (1, 5.8, and 65 bar). Despite the
high chemical and structural diversity of the CoRE MOFs, it was found
that the use of the proposed descriptors significantly improves the
accuracy of the machine learning algorithm, particularly at low pressures,
compared to the predictions made based solely on the rest structural
features. More importantly, the algorithm can be easily adapted for
other types of nanoporous materials beyond MOFs. Convergence of the
predictions was reached even for small training set sizes compared
to what was found in previous works using the hypothetical MOF database.