Impact of Chemical Features on Methane Adsorption by Porous Materials at Varying Pressures

Abstract

Superior performance in methane uptake capacity prediction by hypothetical metal organic frameworks has previously been accomplished using a novel combination of structural and chemical features with machine learning (ML) algorithms. This concept is extended for additional microcrystalline materials, focusing on 69 839 covalent organic frameworks (COFs) and 17 846 porous polymer networks (PPNs). For each material category, data was divided into train (80%) and test (20%) sets. Using the random forest (RF) algorithm, 10-fold cross-validation was carried out to evaluate the robustness of prediction for structural and chemical descriptors. Structural features included surface area, density, and void fraction. Chemical descriptors included the number and type of each atom, electronegativity, and degree of unsaturation among others. When chemical descriptors for adsorption at low pressures were included, significant improvements for predictions were observed compared to solely using structural descriptors. Specifically, adding chemical features increased the R2 value from 0.66 to 0.87 for COFs and from 0.83 to 0.93 for PPNs. These results indicate that inclusion of chemical descriptors improves prediction across materials and pressures. While physisorption is the main driver for adsorption at these pressures, these results also imply contribution of surface chemical motifs on adsorption phenomena.