Because SpookyNet is a complex architecture that includes non-local interactions and empirical corrections, it exhibits slow convergence and the training speed is less correlated with the model performance than SchNet and PaiNN. However, the rank ordering of model configurations for SpookyNet from TPE is still robust (Spearman's ρ = 0.92), which allows for discarding non-optimal model configurations early in training, representing significant computational savings. The goodness of fit metrics for linear regressions using TPE are given in Table .