Semi-empirical (SE) methods strike a balance between classical potentials and density functional theory (DFT) methods in terms of EAST requirements. As the simplest form of electronic structure theory, SE methods employ integral approximations and predefined functional forms of atomic interactions to accelerate calculations. To reduce errors, they incorporate parameterizations based on reliable experimental or theoretical reference data . Although this compromises their accuracy, SE methods remain highly efficient for large systems with 10 2 -10 4 atoms, where classical treatments would be inadequate due to complex QM effects. In this context, ML has the potential to enhance both the accuracy and transferability of SE methods while preserving their computational efficiency .