For each atom, the information about connected atoms and corresponding edges is aggregated, and the resulting vector updates the embedding of the original atom, which allows extracting more complex dependencies. In some implementations, the edges' vectors are also updated with the vectors of connected atoms. (c) For the model to be invariant for the number of atoms, vectors obtained after multiple repetitions of the previous step are aggregated to one vector, usually, the mean or sum of the vectors is used. The new vector now contains the key information about the structure. (d) Finally, this vector passes through a multi-layer perceptron, which predicts the target energy.