On the other hand, the rolling two-probe algorithm is suitable for cavities with larger windows, solving the probing escaping from the cavity issue encountered in the one-probe method. In this approach, the cavity is defined as the volume enclosed by a small probe, which cannot escape, when a second, larger probe blocks the cage's windows (Figure ). However, the two-probe rolling method can fail if the radius of the larger probe is too small, allowing it to move inside the cavity through the cage windows, and resulting in inaccurate volume calculations. In contrast, if the larger probe is too big, the calculated volume is overestimated by an "artificial volume" generated by the gap between the larger probe and the cage atoms. Indeed, this is a significant limitation of the rolling probe algorithm, as the success of the cavity volume calculation depends on the user-defined probe(s) radii, which must be carefully chosen and may not be universally applicable to all systems. Therefore, the results can be improved by a delicate fine-tuning of probe(s) radii in systems with large windows. This process is laborious and difficult to generalize across different systems or when analyzing molecular dynamics (MD) trajectories, where the window sizes dynamically change along the simulation time. Alternatively, one can calculate the largest sphere that fits into the cavity, as done in PyWindow, but this compromises the accuracy of the computed volume as cavities in cages often deviate from a perfect spherical shape.