As for the dataset, we build up our neural network models on boron nanoclusters. Boron clusters show a wide complexity in their structures that it can adopt a variety of geometrical motifs including the planar, quasi-planar, bowl, ribbon, cage, core-shell, fullerene and tubular structures etc. The bonding phenomenon of these clusters is not straightforward as it contains extensive delocalized bonding in both 2D and 3D structures. Boron clusters thus form a challenging dataset for ML problem. Following a successful implementation of deep learning model on boron clusters we would plan to expand the model on other homonuclear clusters that are abundantly reported in the literature. In the present study, we utilize pure boron clusters Bn ranging from the size B4 to B40 to establish the dataset for our machine learning based study. This is due to the fact the structures for the Bn clusters in the size range of n = 4-40 have been relatively well established. Some larger clusters (Bn, with n > 40) still do not have well-established global minimum structures. For example, the B70 boron cluster was postulated to be quasi-planar by Rahane et al. but subsequent studies pointed out that both tubular and bilayer structures have lower energy and are positioned as the global minimum for the B70 cluster. Large clusters can take up a myriad number of configurations and thus it is quite difficult to ascertain the identity of their global minimum structures. Our present training dataset for the Bn boron clusters is thus limited up to the size of B40.