Apart from describing the clusters accurately, it is also essential for the NN to appropriately describe the bulk properties. Fig 7 (a) shows the comparison between the lattice constants predicted by the NN with the ones predicted by DFT. We find that there is an excellent agreement between the NN predictions when compared to DFT. The Cohesive energies predicted by our NN is also in good agreement with the values predicted by DFT as shown in Fig . The correlation between the three unique elastic constants for the ten elements with an fcc crystal structure considered are given in Fig ). The elastic constants for bulk elements also show good agreement with the DFT predicted values even though we do not use elastic constants as part of our NN training set. The elastic constant C 44 posed the biggest challenge as accurately describing the value required the errors in the energies be far more smaller than what we had with our SC trained NN. To circumvent this issue, we augmented our training set with the structures representing the EOS of the elements. Table provides a summary of the performance of the neural network potentials on bulk mechanical properties. We find that the elastic constants are predicted within 10-15 % of the DFT value and were thus able to get an excellent agreement on all three elastic constants as well as the EOS between the ones predicted by NN vs. DFT (Fig. ).