Construction of coarse-grained molecular dynamics with many-body non-Markovian memory

Abstract

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.

References in corpus (4)

• Iterative Reconstruction of Memory Kernels
• Likelihood-based non-Markovian models from molecular dynamics
• Generalized Langevin dynamics simulation with non-stationary memory kernels: How to make noise
• Data-driven construction of stochastic reduced dynamics encoded with non-Markovian features

Cited by in corpus (2)

• Coarse-graining conformational dynamics with multi-dimensional generalized Langevin equation: how, when, and why
• A Gauss-Newton method for iterative optimization of memory kernels for generalized Langevin thermostats in coarse-grained molecular dynamics simulations