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

arXiv 2405.12356 ↗First posted 20 May 2024physics.bio-ph cs.LG physics.chem-phcited by 0

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Abstract (arXiv)

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.

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References · 2

  1. 2017Variational approach for learning Markov processes from time series datacited by 52
  2. 2016Data-driven parameterization of the generalized Langevin equationcited by 14

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