After the grid settings were determined, the ML models were trained by running ArchOnML's dedicated learn-curve generator script in parallel. Focusing on the excitation energies first, it can be seen that the models show a MAE and 2𝜎 err in the order of 0.1 eV, which is roughly in the range of what TD-DFT itself could likely have achieved at best. The coefficients of determination for the triplet state excitation energies reach values as high as 0.93 and 0.83 for the 𝑇 1 and 𝑇 2 state, respectively, showing that these models are reliable.