QM8 encompasses a set of time-dependent density functional theory (TD-DFT) calculations of electronic excited states. The dataset contains 22,000 molecules, which are a subset of QM9 with up to 8 CONF atoms, and further refinement for strained geometries. Computed properties include the vertical excitation energies for the two lowest-lying excited states and corresponding oscillator strengths. For benchmarking, we only trained/tested on the excitation energies at second-order approximate coupledcluster (CC2) 37 /def2-TVZP 38 level of theory, yielding two target variables. We constructed a random train-test split of 90/10 and the validation set was constructed from the training set with a random split of 80/20 for model selection and hyperparameter tuning. Algorithms were trained in a multi-task fashion to predict both properties. QTAIM properties for this dataset were computed at PBE0 39 /def2-TZVP 34 level of theory. Here we aimed to study the efficacy of QTAIM features at higher levels of theory (hybrid functionals via PBE0) given the expense of vertical excitation properties (labels for machine learning).