The perturbational model can also be generalized further by including beyond the direct neighbors for prediction. That is by increasing the depth of neighbors allowed for defining the chemical composition, m i [2], m i [3], and so on, at each perturbative update. This added freedom contributes to significantly lowering the error on the embedding replication as summarized in Table using the RMSE loss metric on the original GNN embedding space.