Having established that the training with SC EAM as a reference is inadequate to accurately describe the nanoclusters, we proceed to transfer the learning based on SC dataset to that from a higher fidelity DFT dataset. We took the training set of the SC converged NN from the previous stage, recalculated their energies using DFT, augmented the training set with 100 highly stable bulk configurations and structures representing the equations of state (EOS) and retrained the network taking into account the angular symmetry functions. Fig. plots the correlation between the performance of the retrained NN on the new training set. The transfer learned NN predictions of energies and the forces for the clusters in the training pool are compared with the reference DFT model predictions. MAE for the training set was found to be less than 20 meV/atom, which is of the same order of magnitude as DFT error.