  Molecular Dynamics (MD) simulation is a powerful tool for understanding the
dynamics and structure of matter. Since the resolution of MD is atomic-scale,
achieving long time-scale simulations with femtosecond integration is very
expensive. In each MD step, numerous iterative computations are performed to
calculate energy based on different types of interaction and their
corresponding spatial gradients. These repetitive computations can be learned
and surrogated by a deep learning model like a Graph Neural Network (GNN). In
this work, we developed a GNN Accelerated Molecular Dynamics (GAMD) model that
directly predicts forces given the state of the system (atom positions, atom
types), bypassing the evaluation of potential energy. By training the GNN on a
variety of data sources (simulation data derived from classical MD and density
functional theory), we show that GAMD can predict the dynamics of two typical
molecular systems, Lennard-Jones system and Water system, in the NVT ensemble
with velocities regulated by a thermostat. We further show that GAMD's learning
and inference are agnostic to the scale, where it can scale to much larger
systems at test time. We also perform a comprehensive benchmark test comparing
our implementation of GAMD to production-level MD softwares, showing GAMD's
competitive performance on the large-scale simulation.
