Machine learning potentials (MLPs) have emerged as promising alternatives to AIMD, reproducing accurate energies and forces from electronic structure reference calculations at a much lower cost. MLPs have been extensively used in modelling materials, organic molecules, and more recently in chemical reactivity. and in electrolytes confined to nanoscale pores. Traditionally, MLPs are trained using AIMD reference data under periodic boundary conditions (PBC). This approach inherently captures long-range interactions but incurs a high computational cost due to the large size of the system, primarily consisting of solvents.