Overall, by taking the average of the energies predicted by the CNN model, we can evaluate the ground state total energy of a particular boron cluster in the test dataset with a a certain accuracy. This approach is not foolproof though, as we are utilizing projections, it is not sensitive to rotational invariance. Since we are utilizing the promolecule density and not the actual ground state electron density, the energy which is outputted from the CNN, will not be the exact ground state energy (E) but a higher energy E՛. Let us stress again that the machine learning method considered here will thus not give accurate energy but is useful for a quick potential energy landscape exploration of nanoclusters. Our model is thus well suited for capturing the energy trend, we thus believe model is better suited for classification-based tasks. Regression model can be converted to classification model by utilizing thresholds i.e. class can be assigned to particular molecule depending whether their predicted energy is above the certain threshold or not. For the classification task, we use a test dataset containing boron clusters ranging from B41 to B50. The global minimum structures and few of their higher energy constitutional isomeric structures are used in this task. We again perform the MD simulation, generating 50 conformers each for the global minimum and their higher energy isomeric counterparts (Table ). We reduce the number of conformation for the classification test dataset as we only require the conformations for getting the average energy of particular nanoclusters for the purpose of classification, also it is less computationally expensive process. The MCC in this case results in 82.3 %, which is a respectable score. In this sense, the CNN classification paradigm is successful in separating out the global minimum out of the different configurational isomers except for one misclassification. This CNN classification paradigm can be then further implemented to augment the efficiency of structure prediction algorithms.