Details

Autor(en) / Beteiligte

• Klippenstein, Viktor
• Wolf, Niklas
• van der Vegt, Nico F. A.

Titel

A Gauss–Newton method for iterative optimization of memory kernels for generalized Langevin thermostats in coarse-grained molecular dynamics simulations

Ist Teil von

• The Journal of chemical physics, 2024-05, Vol.160 (20)

Ort / Verlag

United States: American Institute of Physics

Erscheinungsjahr

2024

Quelle

American Institute of Physics

Beschreibungen/Notizen

• In molecular dynamics simulations, dynamically consistent coarse-grained (CG) models commonly use stochastic thermostats to model friction and fluctuations that are lost in a CG description. While Markovian, i.e., time-local, formulations of such thermostats allow for an accurate representation of diffusivities/long-time dynamics, a correct description of the dynamics on all time scales generally requires non-Markovian, i.e., non-time-local, thermostats. These thermostats typically take the form of a Generalized Langevin Equation (GLE) determined by a memory kernel. In this work, we use a Markovian embedded formulation of a position-independent GLE thermostat acting independently on each CG degree of freedom. Extracting the memory kernel of this CG model from atomistic reference data requires several approximations. Therefore, this task is best understood as an inverse problem. While our recently proposed approximate Newton scheme allows for the iterative optimization of memory kernels (IOMK), Markovian embedding remained potentially error-prone and computationally expensive. In this work, we present an IOMK-Gauss–Newton scheme (IOMK-GN) based on IOMK that allows for the direct parameterization of a Markovian embedded model.