For stage 2, we perform transfer learning by taking the converged training set from stage 1 and recalculating their energies using DFT-PBE. The NN was re-trained using revised fingerprint files which accounted for the radial as well as the angular symmetry operations. The training set was augmented with a set of 100 highly structurally optimized clusters as well as the structures representing the EOS for each of the elements. This was implemented in order to further facilitate the NN in accurately predicting the forces for highly stable structures as well as the bulk elastic constants. The re trained networks were also validated extensively with validation sets of 2000 -3000 structures. To evaluate the equation of state (EOS) plot for all these FCC metals, ± 5% strain was applied in all three directions. The initial bulk structure of fcc system for each of these elements has been collected from materials project database. Three independent elastic constants, were determined for cubic system by employing suitable lattice distortions represented by a strain tensor, ε in such a way that the new lattice vectors r' in the distorted lattice is given by r' = (I + ε)r where I is the unit matrix. This method was adapted from our previous work and the workflow was modified to perform both the active and transfer learning stages.