Coarse-Graining Conformational Dynamics with Multidimensional Generalized Langevin Equation: How, When, and Why.

Area of Science:

• Computational Chemistry
• Molecular Dynamics Simulation
• Statistical Mechanics

Background:

• Coarse-grained (CG) models simplify complex molecular systems.
• Simulating CG conformational dynamics requires methods that maintain consistency with all-atom simulations.

Purpose of the Study:

• To develop a data-driven ab initio generalized Langevin equation (AIGLE) approach.
• To enable learning and simulation of high-dimensional, heterogeneous CG conformational dynamics.
• To ensure CG models are dynamically consistent (DC) with all-atom molecular dynamics.

Main Methods:

• Developed the ab initio generalized Langevin equation (AIGLE) approach.
• Enforced dynamical consistency using the fluctuation-dissipation theorem.
• Proposed criteria for enforcing long-term DC.
• Performed case studies on a toy polymer and alanine dipeptide.

Main Results:

• The AIGLE approach successfully learns and simulates CG conformational dynamics.
• CG models built with AIGLE achieve dynamical consistency with all-atom simulations.
• Practical criteria were established for ensuring long-term DC.

Conclusions:

• AIGLE provides a robust framework for building dynamically consistent CG models.
• The choice between AIGLE and its Markovian limit depends on practical modeling needs.
• This approach is valuable for simulating CG conformational dynamics in complex systems.