A modified version of the graph invariant network (GIN) reported by Kang et al. , was utilized, with Gasteiger charges eliminated from the list of node (atom) features. The number of GIN layers was set to 4 with the embedding dimension set to 200, with the time steps for the readout layer, with average pooling, set to 6 and parameterized with a dropout layer. The output multi layer perceptron (MLP) was modified such that all but the output layer utilized a leaky rectified linear unit (ReLU) activation function. Three hidden layers comprised the output MLP, each with layer normalization. A dropout layer was also present between each hidden layer, excluding the first hidden layer. The GIN layers were also modified such that the hidden layer was also activated by the leaky ReLU function and layer normalized, with the outputs from each GIN layer batched normalized and concatenated before being fed to the readout layer. All dropout layers were parameterized to receive the same dropout ratio during training. Additional details regarding specifics surrounding model parameters such as node and edge embeddings, layer lengths, and regulation can be found in the supporting information.