We have performed high-throughput DFT calculations using Cp2k to compute the energetics of different configurations of AcMX2. Where c is the concentration of different Alkali "A" on the TMD substrate's MX 2 . These energies are used for validation purposes, while a small number of calculations are used for training. The protocol is mentioned in detail in the Results and Discussion section. The featurization and the Machine learning algorithm are discussed here. We begin by computing the atomic point charges of the system using Density-derived atomic point charges(DDAPC) analysis. These charges and structures are used to create electrostatic interactions with the test charge(charge+1). The test charge is moved around to different sites, which yields an interaction energy corresponding to each site. This electrostatic interaction is then broken down into different terms based on radial cutoffs and chemical species as seen in Figure . These electrostatic interaction terms show a high correlation with the target quantity (DFT energy for different sites). Since we are dealing with 2D periodic systems the electrostatic interaction in the equation is subject to the minimum image condition in the lateral directions as shown in Equation .