An important application of this properties dataset is to distinguish between hopping and nonhopping geometries. By filtering the dataset accordingly, the distribution of specific bond distances (such as the C 5 -C 6 bond in fulvene) can be plotted for hopping versus non-hopping geometries. For fulvene, the analysis revealed that the distribution of hopping geometries (represented by red bars) has shifted towards larger C 5 -C 6 bond distances (Figure ). This observation is aligned with the understanding that, at the beginning of the dynamics, the bond stretching in fulvene often leads the system towards a conical intersection, facilitating nonadiabatic transitions. The structural differences between hopping and non-hopping geometries are underscored by the distinct distributions, with hopping geometries predominantly concentrated around a bond distance of approximately 1.55 Å. An alternative method to inspect the dataset and gain insights into its behavior is by plotting bond distances (y-axis) as a function of time (x-axis) (Figure ). This type of analysis, part of the standard routine for understanding nonadiabatic molecular dynamics (NAMD) simulations, helps visualize the evolution of geometrical features such as bond stretching and contraction. Additional information, such as hopping geometries, can be overlaid on this plot. Notably, in this analysis, most hopping points, marked by red indicators, cluster around the maxima of the oscillations in the bond distance. ULaMDyn serves as a tool to facilitate this type of interaction with NAMD simulation results, allowing users to extract and interpret key information efficiently. While these analyses are routine, they lay the groundwork for more advanced unsupervised learning methods, which automate the extraction of deeper insights from high-dimensional data Up to this point, the focus has been on exploring the dataset to identify key features. This exploratory phase allows for experimentation and various tests to understand the dataset better.