We use a NN architecture with 4 layers of neurons. The hidden layers made of 15 nodes each. For stage 1 where we train the networks to a training set obtained using SC EAM forcefields, we use 8 nodes in the input layer. For stage 2 where the network is trained to DFT PBE energies an input layer of 30 nodes is used. These input nodes hold the symmetry functions that represent the potential energy surfaces of the different elements under consideration. The output layer with one node represent the potential energy of a given configuration. Besides, the input layer and the hidden layers contain a bias node that provides a constant signal to all the nodes of its next layer. The spatial coordinates of an atom in a nano structural configuration are mapped into the rotational and translational invariant co-ordinates as