This promolecule density is akin to the initial trial density utilized in DFT calculations. However, unlike trial density in DFT calculations, there is no occurrence of iterative process just the straightforward mapping of promolecule density to energy. One caveat of this approach is since we are not using the actual ground state energy for mapping but promolecule density, thus the energy obtained from the model is not expected to be highly accurate. The purpose of the present machine learning model is to predict energy trends amongst the molecular systems, not the actual energy itself. Ultimately, we want to augment the machine learning algorithm with structure prediction codes, thus the neural network should be able to correctly predict the ordering of structural isomers for a particular molecular system. The model can thus successfully predict the energy ordering of isomers but it will not predict the exact ground state energy of the isomers. The density can be mapped to the ground state energy of a system by using convolutional neural network (CNN) model.