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CGCompiler: Automated Coarse-Grained Molecule Parametrization via Noise-Resistant Mixed-Variable Optimization.

Kai Steffen Stroh1,2, Paulo C T Souza3, Luca Monticelli3

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We introduce a novel mixed-variable particle swarm optimization method for automatically parameterizing coarse-grained force fields (CG FFs). This approach efficiently optimizes both bonded and nonbonded interactions for molecular models like Martini 3.

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Area of Science:

  • Computational Chemistry
  • Molecular Modeling
  • Biophysics

Background:

  • Coarse-grained force fields (CG FFs) utilize building blocks for molecular modeling, offering universality and transferability.
  • Parametrizing molecules in CG FFs involves complex mixed-variable optimization of discrete and continuous parameters.
  • Existing methods often require extensive manual tuning for accurate molecular representation.

Purpose of the Study:

  • To pioneer the use of mixed-variable particle swarm optimization (PSO) for automated molecular parametrization in CG FFs.
  • To simultaneously optimize both bonded and nonbonded interactions within the Martini 3 force field.
  • To demonstrate the method's efficacy by parametrizing the sphingomyelin lipid linker.

Main Methods:

  • Development and application of a mixed-variable particle swarm optimization algorithm.
  • Simultaneous optimization of discrete (nonbonded) and continuous (bonded) parameters.
  • Matching structural (RDFs) and thermodynamic (phase-transition temperatures) data for parametrization.

Main Results:

  • Successful automated parametrization of the sphingomyelin lipid linker using the novel PSO approach.
  • Demonstrated simultaneous optimization of bonded and nonbonded interactions, outperforming other metaheuristic methods.
  • Explored noise-mitigation strategies for improving accuracy in matching phase-transition temperatures.

Conclusions:

  • Mixed-variable PSO offers an efficient and automated solution for parametrizing molecules in building-block CG FFs.
  • The developed method enhances accuracy and reduces manual effort in force field development.
  • This approach holds significant potential for advancing molecular simulations and materials science.