  We present an update of the DScribe package, a Python library for atomistic
descriptors. The update extends DScribe's descriptor selection with the
Valle-Oganov materials fingerprint and provides descriptor derivatives to
enable more advanced machine learning tasks, such as force prediction and
structure optimization. For all descriptors, numeric derivatives are now
available in DSribe. For the many-body tensor representation (MBTR) and the
Smooth Overlap of Atomic Positions (SOAP), we have also implemented analytic
derivatives. We demonstrate the effectiveness of the descriptor derivatives for
machine learning models of Cu clusters and perovskite alloys.
