  Molecular dynamics (MD) simulation predicts the trajectory of atoms by
solving Newton's equation of motion with a numeric integrator. Due to physical
constraints, the time step of the integrator need to be small to maintain
sufficient precision. This limits the efficiency of simulation. To this end, we
introduce a graph neural network (GNN) based model, MDNet, to predict the
evolution of coordinates and momentum with large time steps. In addition, MDNet
can easily scale to a larger system, due to its linear complexity with respect
to the system size. We demonstrate the performance of MDNet on a 4000-atom
system with large time steps, and show that MDNet can predict good equilibrium
and transport properties, well aligned with standard MD simulations.
