In relation to our continuing studies on atomic clusters, we set out to utilize neural networks for our machine learning analysis. Neural networks form a class of ML algorithms which are gaining prominence due to their efficiency, effectiveness and ability to handle a diverse range of problems. Of the neural networks that have been designed for molecular systems, the Behler-Parrinello neural network (BPNN) relies on division of a molecule into its atomic constituent where a feed-forward neural network infers the atomic contributions to the total molecular property. The atomic contributions are then combined to get the total molecular property. In the BPNN scheme, symmetry is preserved by mapping the coordinates onto a large set of two-and three-body symmetry functions. Fixing these symmetry functions is a painstaking endeavour for molecules containing many elements. BPNN is sensitive to the choice of input features used to describe the molecular system, and selection of the most relevant features is usually a time-consuming and challenging task.