The ElemNet model is implemented in TensorFlow and is a 17-layer feed-forward neural network that consists of 4 1024 neuron layers, 3×512 neuron layers, 3×256 neuron layers, 3×128 neuron layers, 2×64 neuron layers, and 1×32 neuron layer. All layers use ReLU activation. For the classification tasks, the output layer is a single neuron layer with sigmoid activation, and the loss function is the binary cross-entropy. The regression task uses an output layer with linear activation and the loss function is the Mean Absolute Error (MAE). The models were trained using the following hyperparameters: a maximum number of epochs of 100, a learning rate of 10 -4 , a batch size of 32, and an L2 lambda of 10 -5 . Twice-repeated 5-fold cross-validation is performed to evaluate the performance of the compound representations on the property prediction tasks. The reported metric on each task is the average MAE and the average AUC (Area under the Receiver-Operator characteristic Curve) for the regression and classification tasks, respectively. The error in the metrics is the standard deviation across the twice-repeated 5-fold cross-validation.