where p 0 is the SOAP vector of the new structure, p i is the SOAP vector of the i-th configuration in the existing set, and ζ is a positive integer that increases the sensitivity of kernel to changes in atomic position. The kernel is computed between the new configuration and all other configurations in the training data set. The selector adds structure to the training set if the maximum value of its similarity vector, K, is smaller than threshold k T , i.e., max(K) < k T . Selecting an appropriate threshold is key as too low values (e.g., similarity below 0.9) can result in the selection of non-physical structures that fail to converge in the self-consistent field (SCF) computations, while too high values (e.g., 1) do not provide any additional information.