Prediction of interaction energy for rare gas dimers using machine learning approaches

In our present work, we applied Machine Learning approaches to predict potential energy profiles for rare gas dimers as well as for the H _2 molecule. We designed an Artificial Neural Network (ANN) model with one and two layers, with two to eight neurons in each layer, to predict potential energy values. We compared the ANN predicted energy values with the ab initio data and we found an excellent agreement between the actual and predicted values. The root mean squared deviation (RMSD) values for the test data are found to be 0.10, 0.22, 0.03 and 0.47 cm ^-1 for He _2 , Ne _2 , Kr _2 and Ar _2 , respectively. Further, we observed that the ANN method is able to fit the potential energy profile for weak van der Waals dimers as well as covalently bound molecules. Graphical abstract Application of machine learning approaches to predict the interaction energies for rare gas dimers is tested in this study. Our results show that Artifical Neural Network modeling is able to predict the energies for these van der Waals interactions.