We introduce a workflow that trains high-dimensional NN to model bulk and cluster properties in 2 stages. In first transfer learning stage, we use the popular Sutton Chen type forcefields to calculate the energy of the configurations that are added to the training set. We first ensure that the Neural network trained using these structures would make predictions closely resembling the ones made by the SC EAM potential. An active learning approach is used to train the NN via 5 major steps: ( In stage 2, the final training set sampled from stage 1 is taken and the energies of the configurations are recalculated using DFT. This training set is augmented with additional actively (on-the-fly) sampled DFT data (clusters vs energetics) until convergence to DFT predicted bulk and cluster energetics is obtained. In this stage, the NN employs a more flexible symmetry functions i.e. accounts for radial as well as angular symmetry functions. This process allows us to take advantage of the radial nature of the SC EAM potential (pair-wise) and in the second stage add corrections (3-body and 4-body) that account for the angular dependence for the potential energies for a nano cluster. The NNs constructed in this study were interfaced with the LAMMPS, VASP and Atomic Energy Network (AENet) software package 58 , which were adapted to implement the transfer and AL schemes outlined above. Simulations using these networks were carried out using AENet interfaced with the Classy Monte Carlo simulation software 59 to perform the sampling during the transfer and AL iterations.