With regards to the baseline models, the energy histogram model employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression , which involved taking an energy histogram that had been converted from energy grids by energy bins. The descriptor-based model utilized 5 geometrical properties (i.e. largest cavity diameter, pore-limiting diameter, gravimetric accessible surface area, volumetric accessible surface area, and volume fraction) as well as 12 chemical properties (i.e. metal type present, number of specified element atoms in unit cell), and 6 additional chemical properties (i.e. total degree of unsaturation, metallic percentage, oxygen to metal ratio, electronegative to total ratio, weighted electronegativity per atom, and nitrogen to oxygen ratio) as inputs. All of these descriptors were used as input to a random forest model. On the other hand, the CGCNN uses atom-based graph representation as inputs, and consists of five convolution layers, one hidden layer after pooling, 64 hidden atom features in convolution layers, and 128 hidden 7 features after pooling.