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Paul N Patrone1, Thomas W Rosch1, Frederick R Phelan1

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Generating transferable coarse-grained (CG) forces is difficult. This study introduces a Bayesian correction workflow that rapidly recalibrates forces, improving efficiency and accuracy for molecular dynamics simulations.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Statistical mechanics

Background:

  • Coarse-grained (CG) molecular dynamics requires transferable forces across state-points.
  • Existing methods for generating CG forces can be computationally expensive and limited in scope.

Purpose of the Study:

  • To develop an efficient and robust coarse-graining workflow for generating transferable forces.
  • To address the challenge of force calibration across multiple state-points in CG simulations.

Main Methods:

  • Introduced a Bayesian correction algorithm utilizing functional derivatives of CG simulations.
  • Employed interpolation schemes to recalibrate initial force estimates (f0) from standard methods like force-matching.
  • Tested the workflow using density-temperature relationships as a model system.

Main Results:

  • Demonstrated efficient computation of physically reasonable force curves on a fine grid of state-points.
  • Showcased the workflow's robustness to various modeling choices, including interpolation schemes and initial force generation tools.
  • Reduced the number of atomistic simulations required for generating CG forces.

Conclusions:

  • The proposed Bayesian correction workflow offers a rapid and inexpensive method for recalibrating CG forces.
  • This approach enhances the transferability and accuracy of CG force fields across diverse state-points.
  • The workflow streamlines the coarse-graining process, reducing computational demands.