Geometry optimization is a crucial computational process to refine the structural arrangement of molecules and materials to their lowest energy configuration . This technique involves adjusting the positions of atoms within a structure to minimize its total energy, ensuring that the system achieves a stable and energetically favourable state . Therefore, by accurately determining the optimal arrangement of atoms, geometry optimization helps to assess the stability and potential reactivity of the metallacage, thereby facilitating the design of efficient and reliable carriers for cisplatin and other therapeutic agents. The example of energy convergence in geometry optimisation has been demonstrated in Figure . The y-axis represents the system's energy in Hartree, while the x-axis denotes the optimization steps. The blue line with circular markers shows the energy values at each optimization step. Initially, there is a significant drop in energy, indicating rapid convergence towards a more stable geometry. As the optimization proceeds, the energy values stabilize, demonstrating that the system has reached a minimum energy configuration. This convergence behaviour is crucial for verifying that the geometry optimization process has successfully identified the lowest energy structure for the cage. The stable energy at the final steps confirms the reliability of the optimized geometry for further analysis.