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Area of Science:

  • Computational Materials Science
  • Polymer Physics
  • Machine Learning Applications

Background:

  • Simulating polymer chain conformations at high fidelity (e.g., united-atom) is computationally expensive.
  • Coarse-grained (CG) models simplify polymers but lose structural detail.
  • Bridging the gap between CG and high-fidelity models is crucial for efficient simulations.

Purpose of the Study:

  • To develop a machine learning framework for reduced-order representations of polymer conformations.
  • To enable accurate reconstruction of high-fidelity united-atom (UA) configurations from coarse-grained (CG) simulations.
  • To establish prerequisites for accelerated polymer dynamics simulations.

Main Methods:

  • Utilized linear singular value decomposition and nonlinear autoencoders to create latent space representations.
  • Developed a linear mapping between CG and UA latent spaces for back-mapping.
  • Employed molecular dynamics relaxation to correct minor structural inaccuracies.

Main Results:

  • Achieved compressed, low-dimensional representations of polymer conformations with minimal structural accuracy loss.
  • Demonstrated a near-perfect linear mapping between CG and UA latent spaces.
  • Successfully reconstructed high-fidelity UA configurations from CG simulations, with inaccuracies corrected by MD relaxation.

Conclusions:

  • The framework provides a compact and accurate latent encoding of polymer conformations.
  • A validated multi-fidelity mapping allows reconstruction of UA structures from CG configurations.
  • This hybrid machine learning-physics approach accelerates polymer simulations and enables efficient super-resolution back-mapping.