This is frequently used to compute, e.g., error calibration curves. Nevertheless, averaging over data points is undesirable, since we aim for a direct correlation of error and uncertainty for a single data point. Machine learning potentials, however, do not only yield a prediction of a single energy value per configuration, but also forces for each atom. Thus, each data point i in a dataset of size N i consists of 3N j force components, where N j is the number of atoms. As a result the model predictions f k ij aim to reproduce the target forces f k ij with k denoting the spatial direction. The predicted forces f k ij are obtained as a committee average over N C committee predictions