Binding site prediction for different physical processes is paramount in computational biophysics and chemsitry. Accurate descriptions of protein-ligand binding sites are essential for rational drug design and structure based virtual screening and target prediction. Ion host guest studies show that multiple quantities like Binding Order, Cooperativity and experimental binding energy are highly dependent on the binding configurations. Alkali binding on substrates non-organic and organic have garnered a lot of attention for their use in optimizing battery technology. The binding energies vary considerably across different binding sites, directly influencing important parameters like the redox potentials and overall stability of the systems. Identifying the most favorable alkali/ligand binding sites in susbtrates involves speculating multiple potential binding locations and calculating the corresponding energies of all possible lithiated structures for comparison. This process is time-consuming and prone to overlooking some viable configurations. For example, Kim et al. identified the energetically favored Li ion positions in 2,6-diaminoanthraquinone by evaluating five potential active sites. Similarly, Zhang et al. used simulated annealing calculations to explore all possible lithiated structures with lower energies. For biomolecular system's this procedure is even more expensive from a computational standpoint. Molecular Electrostatic interactions have been identified to play a key role in the ordering of the binding sites. Molecular Electrostatic Potential (MESP), which is crucial for identifying electrostatic interactions between molecules, can effectively determine binding sites in organic materials. MESP describes the charge distribution around a molecule as influenced by its nuclei and electrons. As a fundamental concept in wave function analysis, MESP has been widely applied to predict nucleophilic and electrophilic reaction sites, as well as to suggest molecular packing arrangements and adsorption behaviors. 12, Liu et.al showed that MESP can be used for visualizing favourable Li binding sites in organic hosts, they correctly identify the stable structures and in turn the voltage profiles for various organic hosts. MESP has site prediction in crystals remains relatively limited. With the rapid development of machine learning (ML)techniques in the field of computaional chemistry and biology. Several studies have employed machine learning algorithms for binding site predictions. However, most of these studies have been limited to biochemical studies like ligand-protien binding. There have been several studies on binding energy prediction for alkalis on 2D substrates, however, these are more concerned with computing the magnitude of binding energy across different alkalis or substrates. These studies address the concentration/coverage of the the alkali atoms on substrate by changing the size of the substrate used. Gong et. al. have used a universal machine learning force field to predict the Li binding site on 2D substrates followed by a similar binding energy prediction done by Dou et al. In our study, we focus on finding the most stable configuration of alkali adsorption (primarily Li) on a 2D TMD substrate(primarily H-MoS 2 ). The standard procedure for finding energetically favorable sites on a substrate requires a significant number of DFT calculations irrespective of the periodicity of the system. Previous studies have shown that atoms prefer the top sites of Mo for octahedral MoS 2 regardless of the size of the system. Hence, we know that Li will occupy the top site irrespective of the concentration. However for a general case Li x MoS 2 , one can come up an exceedingly large number of configurations depending on the positioning of the Li adatoms. The configurations grow even larger if different types of adatoms are present. A simple yet reliable method would be to scan the substrate sequentially (Li x-1 MoS 2 ) using single point DFT calculations to find energetically favorable Li x MoS 2 . However one can see that even these are prohibitive for example to obtain the structure of Li a Mo N/2 S N one would require a*(N-(a-1)/2) single point DFT calculations. All these calculations scan just the top sites of Mo, adding inhomogeneities like defects and substitutions makes things even more complicated. Using DFT calculations previous studies, have observed that in the case of H-Li x MoS 2 , the Li prefers to bind the unoccupied site opposite to a Li atom(Figure ). However, we have observed that these results are system size dependent, which has been shown in Figure . Therefore one needs a*(N-(a-1)/2) DFT calculations to probe favorable configurations of Li a Mo N/2 S N for a particular cell size of MoS 2 .