To bridge this gap between computational cost and accuracy, machine learned interatomic potentials have recently gained popularity in computational chemistry and materials science . In particular, a range of neural network and kernel based potentials have been developed and applied to a wide variety of chemical systems. While somewhat more expensive than classical force fields, these potentials are able to predict energies a) The first two authors contributed equally b) Electronic mail: margarf@fhi.mpg.de and forces with DFT accuracy and have thus become an important part of the toolbox of computational chemistry.