Given the complexity of these dynamics and the high-dimensional nature of molecular geometry data, advanced statistical methods like dimensionality reduction and unsupervised learning are necessary to extract meaningful patterns. ULaMDyn addresses this challenge by automating the analysis of these large datasets, uncovering critical internal coordinates, and allowing researchers to identify key features across molecular trajectories. This concept has already been successfully applied to the analysis of ground-state molecular dynamics data when there is only one trajectory, demonstrating its potential for broader applications. However, the application of unsupervised learning algorithms to analyze nonadiabatic dynamics simulation results remains challenging, with only limited progress made in recent years. Virshup et al. utilized the diffusion map technique for dimensionality reduction in analyzing photoisomerization dynamics through ab initio multiple spawning simulations. Similarly, Belyaev et al. applied the same dimensionality reduction method to examine the geometric evolution in TSH nonadiabatic dynamics. Li et al. explored the geometric evolution in nonadiabatic dynamics using two closely related dimensionality reduction techniques: classical multidimensional scaling (MDS) and isometric feature mapping (ISOMAP), alongside the density-based spatial clustering of applications with noise (DBSCAN) clustering approach. Principal component analysis (PCA) was employed by Peng et al. and Capano et al. to study the photophysics of a Cu-complex in TSH dynamics and to investigate the role of bath motion in the symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian, respectively.