Hirshfeld surfaces have already been utilized for machine learning purposes especially via convolutional neural networks. Logan et al. have utilized deep learning to predict formation energy and lattice structure parameters 27c using Hirshfeld fingerprints as input for the CNNs. Hirshfeld surface can be availed for crystal structure but not for discrete molecules. Promolecule densities is thus required for deep learning predictions of nanoclusters. As the CNN approaches tend to require a large dataset for accurate prediction, we are using a data augmentation technique to increase the data size. Data augmentation is a way of creating new training data from existing training data. In our case, we can use molecular dynamics (MD) simulations to achieve data augmentation, in which the numerous conformers/poses generated by MD simulations are used for augmenting our dataset. MD simulations are accordingly performed using ab initio molecular dynamics (ADMP) simulation. The ADMP simulation turns out to be a robust and computationally efficient method of data augmentation as compared to the random generation of particles and computing the energies of the generated particles. As far as we are aware, prediction of total energies of molecular systems via ML algorithm using the promolecule density has not yet been reported in the current literature.