In nonlinear dimensionality reduction, the relationship between the embedded dimensions and the original features is typically complicated in determining their contribution to clustering patterns. An alternative to gaining intuition about these relationships is to plot each feature of the molecules against the embedded dimensions. For example, plotting the first reduced dimension, X1, against the C 5 -C 6 bond distance reveals a positive correlation (Figure ). In the case of fulvene, this indicates that the X1 coordinate effectively captures the bond stretching trend involving the CH 2 group, which is expected to be one of the key motions for driving the system throughout the conical intersection. This interpretation strategy can be applied to many other quantities to further explore the relationships between geometrical features and the reduced dimensions. Since the analysis performed here is a postprocessing step on the NAMD simulation data, examining only the molecular geometries is not a limitation. Any quantum chemical information available from the simulations can be used as a descriptor for unsupervise d learning analysis. For instance, crucial information on how rapidly geometries can evolve during dynamics is provided by the energy gradient matrices of each potential energy surface. Figure shows the difference between the energy-gradient matrices of the S 1 and S 0 states as an example. Although the ISOMAP diagram derived from the gradient difference descriptors looks different from the one obtained with the R2 descriptor (geometry-based), one can observe that geometries with small and large energy gaps between the S 0 and S 1 states still appear as clearly distinct groups in the plot.