  We investigate the graph-based convolutional neural network approach for
predicting and ranking gas adsorption properties of crystalline Metal-Organic
Framework (MOF) adsorbents for application in post-combustion capture of
$\textrm{CO}_2$. Our model is based solely on standard structural input files
containing atomistic descriptions of the adsorbent material candidates. We
construct novel methodological extensions to match the prediction accuracy of
classical machine learning models that were built with hundreds of features at
much higher computational cost. Our approach can be more broadly applied to
optimize gas capture processes at industrial scale.
