The model thus meets our goal for the utilization in exploration of a potential energy landscape. The model cannot predict the ground state energy with high accuracy as we are mapping the promolecule density to energy instead of the actual electron density. What is important is the ability of the neural network to get the distribution correctly which our model successfully does. This will be important for energy surface exploration of atomic nanoclusters, we can quickly find the initial local stationary structures using our neural network. These structures will be subsequently subjected to quantum chemical calculations to ascertain, or to search further, the global minimum. Overall, our approach shows that the mapping of electron density of a molecule to its ground state energy is possible with the use of neural networks, and such neural network models can be augmented with the available structure generation programs using the genetic approximation search for much faster prediction of the structures using infinitely shorter computing times. A following step of this study which is more interesting but also more challenging, is an effective prediction of the geometric shapes of the stable clusters.