To this end, we introduce suitable performance metrics on which we then base the model evaluation and hyperparameter optimization. These metrics are directly related to the targeted observables, but are universally applicable across nuclear species, less prone to noise, and rescaled to all lie within a comparable range. Use of such metrics instead of the observables themselves is particularly relevant for an efficient learning of diverse databases that comprise different quantities of different chemical species, like in the present showcase where we target the simultaneous learning of several key NMR observables for the three constituent LTO species Li, Ti, and O.