Here ϕ(x) is a linear densely connected layer and f res (ϕ(x)) allows for residual connections against vanishing gradients and is used as a building block for f e (•). This was empirically found to improve performance over the vanilla multilayer Perceptron architecture . The choice of non-linear activation function used information to produce a separate set of graph-level reciprocal-space features. These are all passed to the readout function after aggregation and produce a final prediction as follows