We evaluate the performance of the first stage of our Transfer/Active learning workflow, where the NN was trained to the SC EAM potential. As part of the training, note that only the radial symmetry functions were taken into consideration using the fingerprint file. We can also observe that the error drops rapidly in the initial stages of the AL and going forward, the addition of more structures slows down and subsequently, the fine tuning of the network begins. Eventually the NN reaches a stage where more structures are no longer added i.e. the NN errors are within the specified tolerance (< 20 meV/atom). The size of the training set as well as the number of AL iterations needed to reach the convergence for each of these elements are influenced by a variety of factors including the range of energies over which the configurations are sampled (from near to far-from-equilibrium) and the complexity of the configuration space the network is trying to learn.