As stated above, the dataset used for training the model is generated from boron clusters ranging from the size of B4 to B40. Ab initio based molecular dynamics simulations (ADMP) are performed on all these clusters considered. Each ADMP trajectory gives a set of molecular clusters along with their corresponding energies. There are thus 37 clusters in the dataset and each cluster generates 1020 structures along with their corresponding energies, thus we obtain 37,740 structures. For each of these 37,740 structures, we generate the corresponding promolecule density for each structure using the Crystal Explorer (Figure ). The promolecule density is a 3D quantity and cannot be utilized by 2D CNN; of course there do exist 3D CNNs which can directly work with the promolecule density but we do not use 3D CNNs as they possess an inherent disadvantage, that is, they have high computational complexity and excessive memory usage. We thus limit our study to 2D CNN to utilize the promolecule density for our model we project this 3D quantity to three axes X, Y & Z to get 2D images in the YZ, XZ and XY plane respectively (cf. Figure ). Projection of the surface on the three orthogonal planes ensure that we are capturing maximum data from the 3Dl surface. Projection of the promolecule density to images is done via a pointcloud2image Matlab script. Thus, from 33,740 structures generated, we get 113,220 projected images, hence we obtain a dataset of a size of 113,220. The dataset obtained consists of projected images from planar, quasi planar, bowl, tubular, cage and fullerene structure (cf. Table ). A quite complicated growth pattern is seen as we move from B4 to B40. Planar, quasiplanar and bowl structures are predominant (67.6%) followed by tubular (18.9%), cage (8.1%) and fullerene (5.4%) structures. The dataset is divided into the training, validation and test set in the following proportions of 60, 20 and 20%, respectively.