In order to assess how k-d tree insertion scales for a variety of common solvents, we performed a benchmark study where a single molecule (e.g., water, ethanol, methanol, etc.) was both the "solute" and "solvent." We systematically varied the box padding from 2 to 6 nm, thereby creating increasingly large volumes to be filled, and we set the target densities to match each solvent's experimental value (e.g., 998 kg/m 3 for water, 789 kg/m 3 for ethanol, etc.). For each padding size and solvent combination, the total time to insert all requisite molecules was recorded along with the resulting number of inserted molecules. The dynamic batch approach was employed, ensuring that the frequency of tree rebuilding was reduced compared to a one-by-one update strategy. Figure shows the aggregated results across all tested solvents, with a comparison against linear and quadratic reference lines on a log-log scale. The majority of data points exhibit nearly linear growth with respect to the number of inserted molecules, illustrating how dynamic batch insertion lowers the computational overhead of repeated k-d tree rebuilding. In other words, instead of rebuilding the tree for every single added molecule, it is reconstructed only once after each batch, which leads to a scaling closer to O(N ) rather than O(N 2 ). Figure provides a more fine-grained view of each solvent. Polar solvents (e.g., water, methanol, acetone) and non-polar solvents (e.g., hexane, toluene) tend to exhibit similarly shaped insertiontime curves, suggesting that random orientation and molecular geometry-rather than specific functional groups-play the most significant role in determining efficiency. Notably, all solvents display a roughly linear or slightly superlinear scaling trend, confirming that the dynamic batch approach can handle a variety of chemical species without major performance penalties. Overall, these results demonstrate that k-d tree-based collision detection remains robust and efficient over a wide range of solvents and box sizes, particularly when dynamic batching is used.