In this study, we show that a machine learning(ML) model can be used to reduce the number of DFT calculations. As mentioned earlier, Previous studies have focussed on predicting the binding energy of alkali atoms on different TMD substrates. Simplistic models like linear regression have performed flawlessly for this task with simple features like LUMO, ionization energy(alkali), and cohesion energy. However, these studies are more inclined towards finding the binding energies for an unknown TMD substrate and alkali adsorbent. There are no site-dependent features, hence it will give the same binding energy for different configurations of Li x MoS 2 . Therefore as discussed in the methods section we have broken down the electrostatic interaction between the adsorbate and substrate into terms depending on spherical cutoffs and chemical species. These terms shown in Equation 2 are used as features for the Machine Learning model.