  We introduce a machine-learning-based coarse-grained molecular dynamics
(CGMD) model that faithfully retains the many-body nature of the
inter-molecular dissipative interactions. Unlike common empirical CG models,
the present model is constructed based on the Mori-Zwanzig formalism and
naturally inherits the heterogeneous state-dependent memory term rather than
matching the mean-field metrics such as the velocity auto-correlation function.
Numerical results show that preserving the many-body nature of the memory term
is crucial for predicting the collective transport and diffusion processes,
where empirical forms generally show limitations.
