GemNet potentials were trained on atomization energies (AE) and forces (F) simultaneously. Since forces are ultimately the driver of MD simulations and contain more fine-grained information than energies, forces were weighted more strongly in our fits, so that the AE only contributes 0.1% to the loss function (see SI for details). This essentially follows the philosophy of gradient domain machine learning, which exclusively uses forces. However, we do include a small AE contribution to the loss, as energy differences across chemical space cannot be learned effectively from forces alone. For training, the QM7-x dataset was randomly split into a test set of 10,100 configurations, training sets of 3.2k, 32k, 320k and 3.2Mio configurations and corresponding validation sets of 800, 8k, 80k and 800k configurations (the latter being used for hyperparameter selection, see SI). In the interest of simplification, we will denote models trained on small (3.2k and 32k) and large (320k and 3.2Mio) training sets as 'sparse' and 'exhaustive' models respectively.