Table show how well the perturbative replicates reproduce the true embedding vector for alkenes only using both the neighbor test and the mean distance to the true embedding. The lat- Table Results of the neighbor test and mean distance to true embedding for alkene embeddings in the QM9 test set (341 molecules), after successive application of the perturbational updates to form the embedding replicates. The updates were fitted with both linear and non-linear regression. For non-linear we used neural networks with two architectures, one with a non-linear 'tanh' function on the 128-feature replicate ('nonlinear 1'), and another one with an additional linear layer ('non-linear 2'). If the embedding lies nearest to its true GNN embedding then that counts as a success to the neighbor test. The table also compares the mean distance to the true GNN embedding, which can be compared to the mean minimum distance between alkene embeddings (1.79) or compared to the mean distance between any two alkene embeddings (9.46).