  Molecular dynamics is a powerful simulation tool to explore material
properties. Most of the realistic material systems are too large to be
simulated with first-principles molecular dynamics. Classical molecular
dynamics has lower computational cost but requires accurate force fields to
achieve chemical accuracy. In this work, we develop a symmetry-adapted graph
neural networks framework, named molecular dynamics graph neural networks
(MDGNN), to construct force fields automatically for molecular dynamics
simulations for both molecules and crystals. This architecture consistently
preserves the translation, rotation and permutation invariance in the
simulations. We propose a new feature engineering method including higher order
contributions and show that MDGNN accurately reproduces the results of both
classical and first-principles molecular dynamics. We also demonstrate that
force fields constructed by the model has good transferability. Therefore,
MDGNN provides an efficient and promising option for molecular dynamics
simulations of large scale systems with high accuracy.
