In ULaMDyn, the DimensionReduction() class is used to perform dimensionality reduction on the descriptor dataset provided as input. To minimize the correlation between consecutive geometries, the dataset can be resampled at larger time intervals. For instance, a time step of 0.5 fs, five times larger than the time step used to generate the NAMD trajectories, is employed to reduce temporal correlation, enhancing the algorithm's ability to identify meaningful patterns. The DimensionReduction() class provides access to several methods for dimensionality reduction with ISOMAP, a nonlinear technique, being utilized here for demonstration. This method has proven particularly effective in identifying critical active coordinates that dominate photophysical processes in nonadiabatic simulations of complex molecular systems. The dataset is reduced from 66 dimensions to two by the ISOMAP method for visualization. A neighborhood graph with a specified number of neighbors (e.g., 30 Figure ) is constructed by the ISOMAP method, and this graph is used to map high-dimensional data into a lower-dimensional space. A distribution is revealed by the plot shown in Figure (b), where points with similar properties (e.g., energy gaps) are clustered together despite this information not being explicitly provided as input to the method. Important geometries and regions of interest within the dataset are easily identified through this visualization, which condenses information on independent NAMD trajectories. Although the selection of molecules based solely on properties such as the energy gap is possible, it is noted that this approach may not always capture nuanced differences in geometry . A more comprehensive view is provided by the dimensionality reduction diagram (Figure ), which highlights distinct regions and aids in the selection of significant molecular geometries for further analysis.