For describing the elemental properties at each site, we use two separate property sets. First, we use the one-hot encoded elemental properties from the crystal graph convolutional neural network (CGCNN) to give us a row vector of 138 columns. The properties used are; electronegativity, covalent radius, valence electrons, first ionization energy, electron affinity, block and atomic volume. As shown in figure , the first row contains the unit cell information and the last row of the representation signifies whether the structure is stable (1) or not (0). In between the rows contain the information of the lattice sites, i.e. the group, period, fractional coordinates and elemental properties. Thus, a matrix of (N+2,138) size was created which contains the information of the crystal structure as well as the elemental properties. N is the maximum number of sites present in a unit cell in the entire dataset. This is referred to as Rep2.