  We apply a temporal edge prediction model for weighted dynamic graphs to
predict time-dependent changes in molecular structure. Each molecule is
represented as a complete graph in which each atom is a vertex and all vertex
pairs are connected by an edge weighted by the Euclidean distance between atom
pairs. We ingest a sequence of complete molecular graphs into a dynamic graph
neural network (GNN) to predict the graph at the next time step. Our dynamic
GNN predicts atom-to-atom distances with a mean absolute error of 0.017 \r{A},
which is considered ``chemically accurate'' for molecular simulations. We also
explored the transferability of a trained network to new molecular systems and
found that finetuning with less than 10% of the total trajectory provides a
mean absolute error of the same order of magnitude as that when training from
scratch on the full molecular trajectory.
