Using those elaborate datasets, models can be built, that describe a correlation between an input vector, also referred to as "labels", and a target property. During the training phase, a statistical model is optimized to minimize the loss function for predicting the target based on the labels. The complexity of these labels can vary drastically from basic proper-ties like the molecular weight of a molecule to electronic properties such as the Hamiltonian matrix or orbital energies. The labels should represent the necessary chemical information of a molecule to predict the target property. The computation of such labels should be efficient since they are computed not only during the training phase but also during the prediction or inference phase. As discussed in the previous paragraph, xTB methods are commonly used for structure optimizations when generating datasets. The use of labels based on xTB properties is therefore associated with no additional computational cost.