Another important machine learning based potential is the gradient domain machine learning (GDML) . In the GDML scheme, the symmetry is preserved by mapping the coordinates onto the eigenvalues of the Coulomb matrix whose elements are the inverse distances between all distinct pairs of atoms. However, GDML has up to now only been used for relatively small organic molecules. In lieu of the aforementioned shortcomings, we set out to look at other approaches in devising a new methodology involving neural networks. It has been shown that the neural network consisting of single hidden layer can approximate any continuous function and hence neural networks are also termed universal function approximators. This makes neural networks particularly suitable for regression problems. It is well known that the deep convolutional networks can approximate any continuous function. Featureless learning is the unique ability of convolutional neural network (CNN) to utilize training data, especially images, directly for modelling without feature extraction. Featureless learning is also expected to be useful for the creation of a regression model which not only predicts energy but also multiple properties of molecular systems.