Here, the molecule on the left has a rather dark 𝑆 1 state ( 𝑓 Osc (𝑆 1 ) = 0.03) as the hole wavefunction does not follow the desired 𝜋 -𝜋 * excitation character. For a different molecule with a bright 𝑆 1 state ( 𝑓 Osc (𝑆 1 ) = 0.41), however, the desired transition character is fulfilled (right hand side of Figure ). Assuming that these differences can be expressed with the SQC wavefunction based descriptors, a comparison-based ML approach such as KRR, will assign each probe molecule to one of these cases. The presence of more than one excitation character within the collective set of "𝑆 1 excitations", however, effectively reduces the amount of available samples for each different possible state characterization. Ultimately, this means that a model for impure states requires a relatively larger amount of training data to achieve similar predictive power as when working with pure states. Comparing the learning curve statistics for the 𝑇 1 and 𝑆 1 excitation energies (cf. Figure )