There are many different types of machine-learning (ML)-based potential methods available . These include Behler-Parrinello neural network potentials (BPNN) , Gaussian approximation potentials (GAP) , spectral neighbor analysis potentials (SNAP) , ANI-1 , SchNet , Gradient Domain Machine Learning (GDML), and Bonds-in-Molecules Neural Network . We used the DeePMD formalism because of its scalability and computational efficiency. It differs from several other ML-based potential. For example, the Bonds-in-Molecules Neural Network method requires empirical information of chemical bonds for training, while DeePMD only needs information on chemical species and atomic coordinates. As a result, DeePMD requires less preliminary information about the system. The GDML scheme uses a global descriptor that does not exhibit size-extensivity, whereas DeePMD uses localized descriptors to ensure size-extensivity. Furthermore, the BPNN model necessitates human intervention for crafting local symmetry functions as descriptors, increasing the risk of errors. DeePMD is an end-toend method that does not require human intervention, minimizing errors that may arise from human involvement.