  Classical molecular dynamics simulations are based on solving Newton's
equations of motion. Using a small timestep, numerical integrators such as
Verlet generate trajectories of particles as solutions to Newton's equations.
We introduce operators derived using recurrent neural networks that accurately
solve Newton's equations utilizing sequences of past trajectory data, and
produce energy-conserving dynamics of particles using timesteps up to 4000
times larger compared to the Verlet timestep. We demonstrate significant
speedup in many example problems including 3D systems of up to 16 particles.
