Through consecutive steps of message passing, the graph nodes gather information from nodes that are further and further away, which implicitly accounts for many-body interactions. Global pooling layers are connected at the end of a stack of consecutive graph convolutional layers and aim at aggregating the node feature associated with each atom across a graph into a single feature. This is achieved by summing the local interactions of each atom with its neighbors and using the result to estimate global properties. Finally, fully connected (FC) layers take the results of pooling, i.e., the extracted features, and provide the output prediction for global properties.