where U E is the electrostatic energy, while NN is the nearest neighbor and SS denotes sameside as shown in Figure . The electrostatic formula shows a significant improvement over the simple sum of partial charges on sulphur sites for the 4x4 system across different concentrations as shown in Figure . However, it turns out that the correlation between the DFT energies and the electrostatic energies computed with the above formula is very much dependent on the system size as shown in Figure . Modifications to the electrostatic formula by increasing the number of neighbors do not provide any significant improvement (Figure ). Note that binding site prediction is more important for larger system sizes due to the higher computational cost associated with the corresponding DFT calculations. In the subsequent subsection we introduce a simple machine learning model based on the electrostatic energies of the neighbors with changing certain cutoff as features that can generalize over a wide range of concentration and system size.