Machine learning (ML) is a powerful emerging technique for the construction of molecular transferrable and non-transferrable potentials. In the field of computational chemistry, ML has currently and extensively been implemented to solve problems in a variety of chemical subjects such as, among others, the prediction of structures, reaction pathways, formation energies, nuclear magnetic resonance (NMR) shift, prediction of HOMO-LUMO gap and glass transition temperature... Several ML algorithms have already been reported in the recent literature that deal with molecular systems including the neural networks, support vector machine (SVM) based classification/regression, ridge regression, gaussian progression regression (GPR), partial least square (PLS) based regression, random forest/XG boost etc.