  We describe version 2 of the SPICE dataset, a collection of quantum chemistry
calculations for training machine learning potentials. It expands on the
original dataset by adding much more sampling of chemical space and more data
on non-covalent interactions. We train a set of potential energy functions
called Nutmeg on it. They use a novel mechanism to improve performance on
charged and polar molecules, injecting precomputed partial charges into the
model to provide a reference for the large scale charge distribution.
Evaluation of the new models shows they do an excellent job of reproducing
energy differences between conformations, even on highly charged molecules or
ones that are significantly larger than the molecules in the training set. They
also produce stable molecular dynamics trajectories, and are fast enough to be
useful for routine simulation of small molecules.
