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Data-driven coarse-grained modeling of polymers in solution with structural and dynamic properties conserved.

Shu Wang1, Zhan Ma1, Wenxiao Pan1

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We developed a data-driven coarse-grained (CG) model for polymers in solution that accurately captures dynamic and structural properties. This advanced CG modeling conserves essential characteristics of atomistic systems.

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Area of Science:

  • Computational chemistry and materials science
  • Polymer physics and simulations

Background:

  • Atomistic simulations provide detailed polymer behavior but are computationally expensive.
  • Coarse-grained (CG) modeling simplifies systems but often struggles to conserve dynamic and structural properties.
  • The generalized Langevin equation (GLE) framework offers a path for accurate CG modeling.

Purpose of the Study:

  • To develop a data-driven coarse-grained (CG) modeling approach for polymers in solution.
  • To ensure the CG model conserves both dynamic and structural properties of the atomistic system.
  • To enhance computational efficiency in polymer simulations.

Main Methods:

  • Utilized the generalized Langevin equation (GLE) framework.
  • Employed a two-stage Gaussian process-based Bayesian optimization to infer the non-Markovian memory kernel from velocity autocorrelation function (VACF) data.
  • Developed an active learning method to identify hydrodynamic scaling in VACF and memory kernel, accelerating kernel inference.
  • Compared deep learning and iterative Boltzmann inversion for constructing the CG potential.
  • Mapped the GLE onto an extended Markovian process for computational efficiency.

Main Results:

  • The proposed CG modeling robustly and accurately reproduced dynamic and structural properties of polymers in solution.
  • Demonstrated conservation of key properties compared to reference atomistic simulations.
  • Assessed accuracy and computational efficiency using a model star-polymer solution system across various concentrations.

Conclusions:

  • The developed data-driven CG modeling approach effectively captures polymer dynamics and structures in solution.
  • The method offers a computationally efficient alternative to atomistic simulations while maintaining high accuracy.
  • This approach provides a powerful tool for studying complex polymer systems.