  The accuracy of any machine learning potential can only be as good as the
data used in the fitting process. The most efficient model therefore selects
the training data that will yield the highest accuracy compared to the cost of
obtaining the training data. We investigate the convergence of prediction
errors of quantum machine learning models for organic molecules trained on
energy and force labels, two common data types in molecular simulations. When
training and predicting on different geometries corresponding to the same
single molecule, we find that the inclusion of atomic forces in the training
data increases the accuracy of the predicted energies and forces 7-fold,
compared to models trained on energy only. Surprisingly, for models trained on
sets of organic molecules of varying size and composition in non-equilibrium
conformations, inclusion of forces in the training does not improve the
predicted energies of unseen molecules in new conformations. Predicted forces,
however, also improve about 7-fold. For the systems studied, we find that force
labels and energy labels contribute equally per label to the convergence of the
prediction errors. Choosing to include derivatives such as atomic forces in the
training set or not should thus depend on, not only on the computational cost
of acquiring the force labels for training, but also on the application domain,
the property of interest, and the desirable size of the machine learning model.
Based on our observations we describe key considerations for the creation of
datasets for potential energy surfaces of molecules which maximize the
efficiency of the resulting machine learning models.
