Coarse-graining conformational dynamics with multi-dimensional generalized Langevin equation: how, when, and why
This work addresses the challenge of simulating complex molecular dynamics more efficiently for computational chemistry and biophysics, though it appears incremental as it builds on existing generalized Langevin equation methods.
The researchers tackled the problem of modeling high-dimensional, heterogeneous coarse-grained conformational dynamics by developing a data-driven ab initio generalized Langevin equation (AIGLE) approach, which builds models in dynamical consistency with all-atom molecular dynamics and includes criteria for long-term consistency, as demonstrated in case studies of a toy polymer and alanine dipeptide.
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.