In summary, we have introduced a workflow that trains high quality NN to learn the underlying physics of inter-atomic and intermolecular interactions from existing classical poten-tial models and transfer the learning to a network that can be retrained with higher fidelity DFT dataset. Such an approach is shown to overcome the limitations of pre-defined functional forms employed in classical MD -our approach builds on the physical models and incorporates flexibility as well as accuracy via retraining with a DFT dataset. The transfer learning workflow also includes an active learning loop that generates a NN potential that does an excellent job of replicating DFT energies over an elaborate energy range for 10 different elemental systems, while using sparse training data sets. Our method first learns the radial contributions to the potential energy surface of the 10 elements using the SC EAM potentials followed by transfer learning of the network using DFT as the reference that also include the angular contributions.