Graph isomorphism network (GIN) models followed the architecture proposed by , where the calculation of consecutive hidden layers parallels Eq. ( ), with two key differences: the self loops are given a weight different from unity, which may be a learnable parameter (1+ε); and the activation function is preceded by a multilayer feedforward neural network. In this study, the parameter ε was learned and a two-layer neural network was used. Training and validation of the model were done using 85 and 15% of the training set; training was stopped once the validation loss had reached a minimum.