To further assess our model, we use two well-known quantities in machine learning realm: correlation coefficient R and regression coefficient R 2 . The Pearson correlation coefficient captures how similar is the data distribution of predicted response to the actual response, whereas the R 2 coefficient is a scale independent quantity used for assessing a regression model. Since our model is trained on regression loss, R 2 is pertinent for gauging the model performance. The R 2 and Pearson correlation coefficient value of 1 are for a perfect model which accurately predicts all data points correctly. We also utilize these two quantities as they are ideal for capturing the distribution or trend of energy distribution amongst different molecular systems, thus it will allow us to gauge how well the model can capture the ordering of the clusters with respect to their energies. The R 2 regression coefficients amount to 0.86 and 0.89, respectively, and the correlation coefficients are 0.93 and 0.94 for the validation and test data set, respectively (cf. Table .). The CNN model is thus able to learn from the projected images of the electronic densities and able to map the density features obtained from the images to evaluate the total energies quite accurately. Figure displays the results demonstrating that the CNN model predicts quite well for each of the clusters, though a large variance is observed for B14 and B17 clusters. This figure may not be very intuitive to the readers as it includes up to 1020 conformers for certain large clusters considered, and thus for each corresponding cluster a spread of predicted energy values is seen. While the predicted energies vary over a range for the configurations of a particular cluster, with exception of one cluster B17, most of the values of the configuration of particular cluster are clustered towards the mean; the presence of outliers at both ends of distribution is akin to a bell distribution, where most of the values congregate around the mean and outlier are present at both ends of the Gaussian distribution. Taking the mean of the values gives us the approximate value of the cluster, similar to Gaussian distribution. A better way of visualization is plotting the average conformers energy of nanocluster with respect to the average predicted energy for each cluster as presented in Figure . We can see in Figure that all the three values of R 2 and R are improved, with an exception for the B17 cluster; all the average predicted energies lie close to regression line. The average energy of conformers (Eavg_conformers) of a particular nanocluster is roughly equivalent to the ground state energy of nanocluster Egs (Eavg_conformers ≈ Egs).