English

Coarse-graining conformational dynamics with multi-dimensional generalized Langevin equation: how, when, and why

Biological Physics 2024-09-13 v1 Machine Learning Chemical Physics Data Analysis, Statistics and Probability

Abstract

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.

Keywords

Cite

@article{arxiv.2405.12356,
  title  = {Coarse-graining conformational dynamics with multi-dimensional generalized Langevin equation: how, when, and why},
  author = {Pinchen Xie and Yunrui Qiu and Weinan E},
  journal= {arXiv preprint arXiv:2405.12356},
  year   = {2024}
}
R2 v1 2026-06-28T16:33:37.133Z