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Conservative Potentials for a Lattice-Mapped Coarse-Grained Scheme.

Siwei Luo1, Mark Thachuk1

  • 1Department of Chemistry, University of British Columbia,Vancouver V6T 1Z1, Canada.

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This study introduces a novel coarse-grained (CG) mapping scheme for atomistic particles, revealing a generalized quadratic potential form. This approach demonstrates potential transferability for complex systems in molecular dynamics simulations.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Statistical mechanics

Background:

  • Coarse-graining (CG) simplifies complex molecular systems by representing groups of atoms as single particles.
  • Traditional CG methods often rely on fixed mapping schemes, limiting their adaptability.
  • Developing accurate and transferable CG potentials is crucial for simulating larger systems.

Purpose of the Study:

  • To investigate a novel, dynamic coarse-graining (CG) mapping scheme for nonbonded atomistic particles.
  • To determine the functional form of the resulting conservative potential.
  • To assess the transferability of the developed CG potential.

Main Methods:

  • Employed a bottom-up approach, mapping atomistic particles to fluid element-like subcells on a cubic lattice.
  • Utilized dynamic labeling, allowing CG particle labels to change on-the-fly.
  • Calculated equilibrium atomistic molecular dynamics trajectories for Lennard-Jones fluids and converted them to CG trajectories.

Main Results:

  • CG probability distribution functions were calculated from converted trajectories.
  • Correlation studies revealed uncoupled position and mass variables within subcells.
  • The CG potential was found to follow a generalized quadratic form, well-described by a multivariate Gaussian distribution, with strong couplings to neighboring cells.

Conclusions:

  • The dynamic CG mapping scheme yields a generalized quadratic potential, analytically derivable.
  • The derived potential form is robust to variations in CG scheme details and atomistic potentials.
  • The approach shows significant promise for transferability to more complex systems, with future work planned for fuzzy boundary schemes.