  Many important tasks in chemistry revolve around molecules during reactions.
This requires predictions far from the equilibrium, while most recent work in
machine learning for molecules has been focused on equilibrium or
near-equilibrium states. In this paper we aim to extend this scope in three
ways. First, we propose the DimeNet++ model, which is 8x faster and 10% more
accurate than the original DimeNet on the QM9 benchmark of equilibrium
molecules. Second, we validate DimeNet++ on highly reactive molecules by
developing the challenging COLL dataset, which contains distorted
configurations of small molecules during collisions. Finally, we investigate
ensembling and mean-variance estimation for uncertainty quantification with the
goal of accelerating the exploration of the vast space of non-equilibrium
structures. Our DimeNet++ implementation as well as the COLL dataset are
available online.
