  A data-driven ab initio generalized Langevin equation (AIGLE) approach is
developed to learn and simulate high-dimensional, heterogeneous, coarse-grained
conformational dynamics. Constrained by the fluctuation-dissipation theorem,
the approach can build coarse-grained models in dynamical consistency with
all-atom molecular dynamics. We also propose practical criteria for AIGLE to
enforce long-term dynamical consistency. Case studies of a toy polymer, with 20
coarse-grained sites, and the alanine dipeptide, with two dihedral angles,
elucidate why one should adopt AIGLE or its Markovian limit for modeling
coarse-grained conformational dynamics in practice.
