coarse-grained-md

Coarse-Grained MD

Description

This skill covers coarse-grained (CG) molecular dynamics: mapping atomistic structures to CG bead representations, parameterizing CG force fields (Martini 3, iterative Boltzmann inversion, force matching), running CG simulations in GROMACS and LAMMPS, backmapping CG configurations to atomistic resolution, and validating CG models against atomistic and experimental reference data. CG-MD replaces groups of heavy atoms with single interaction sites, reducing degrees of freedom by 2–10× and enabling simulations on spatial scales of 10–1000 nm and temporal scales of microseconds to milliseconds. Invoke this skill when the relevant length or time scale exceeds what all-atom MD can access within the computational budget, or when high-throughput screening of molecular assembly, polymer morphology, lipid bilayer properties, or protein–membrane interactions is required.

Domain Context

Coarse-graining is not simply a faster simulation; it is a change of the physical model. A CG bead represents 2–10 heavy atoms and the associated hydrogen atoms as a single spherical interaction site with effective mass, position, and interaction parameters. The reduction in degrees of freedom comes at a cost:

• Loss of chemical detail: CG representations cannot distinguish stereoisomers, tautomers, or conformations at the bond-angle level. The CG bead type is the resolution limit of the chemical identity.
• Modified dynamics: CG potentials are free-energy surfaces, not potential energy surfaces. The smoother landscape increases conformational sampling but accelerates dynamics beyond the true atomistic rates. The CG time scale is related to the atomistic time scale by a system-dependent, temperature-dependent, and observable-dependent factor (typically 4–8× for Martini water). [EXPERT REVIEW NEEDED]
• Non-uniqueness of the mapping: Many different atom-to-bead assignments are consistent with the same molecular structure. Different mappings produce different CG potentials, different dynamics, and potentially different thermodynamic observables. There is no uniquely correct CG representation.
• Transferability: CG potentials fitted to reproduce one thermodynamic state (temperature, pressure, composition) may not transfer to another. Martini 3 is parameterized for biomolecules near 310 K, 1 bar, with explicit CG water. Using it at 500 K or with implicit solvation is extrapolation.
• Bottom-up vs. top-down: Bottom-up CG potentials (IBI, force matching, relative entropy) are derived systematically from all-atom reference simulations. They reproduce the atomistic structural and thermodynamic reference data at the parameterization state point but may have limited transferability. Top-down CG potentials (Martini) are parameterized to reproduce experimental thermodynamic data (partitioning, phase behavior, structure) and are more transferable across compositions but less precise for a specific system.