  Molecular dynamics (MD) simulation is essential for various scientific
domains but computationally expensive. Learning-based force fields have made
significant progress in accelerating ab-initio MD simulation but are not fast
enough for many real-world applications due to slow inference for large systems
and small time steps (femtosecond-level). We aim to address these challenges by
learning a multi-scale graph neural network that directly simulates
coarse-grained MD with a very large time step (nanosecond-level) and a novel
refinement module based on diffusion models to mitigate simulation instability.
The effectiveness of our method is demonstrated in two complex systems:
single-chain coarse-grained polymers and multi-component Li-ion polymer
electrolytes. For evaluation, we simulate trajectories much longer than the
training trajectories for systems with different chemical compositions that the
model is not trained on. Structural and dynamical properties can be accurately
recovered at several orders of magnitude higher speed than classical force
fields by getting out of the femtosecond regime.
