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Multiscale modeling bridges different scales to understand polymer behavior. This review covers computational methods and strategies for simulating complex polymeric materials, highlighting current challenges and future directions.

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Area of Science:

  • Polymer Science and Engineering
  • Computational Materials Science
  • Multiscale Modeling

Background:

  • Polymeric materials exhibit complex behaviors arising from phenomena across multiple length and time scales.
  • Understanding these hierarchical structures is crucial for advancing polymer systems.
  • A multiscale analysis is essential to capture the inherent multiscale nature of polymers.

Purpose of the Study:

  • To provide a comprehensive overview of recent developments in multiscale modeling and simulation of polymeric materials.
  • To review computational methods at various scales (quantum mechanical, atomistic, mesoscopic, macroscopic).
  • To discuss different multiscale strategies, including sequential, concurrent, and adaptive resolution schemes.

Main Methods:

  • Review of computational techniques: quantum mechanics, Monte Carlo, molecular dynamics, Brownian dynamics, dissipative particle dynamics, lattice Boltzmann method, finite element, and finite volume methods.
  • Detailed discussion of multiscale strategies: sequential (coarse-graining, backmapping), concurrent (handshaking, energy/force-based coupling), and adaptive resolution schemes.
  • Illustration of applications in polymer science with examples.

Main Results:

  • Overview of established and emerging computational methods for different scales.
  • Analysis of sequential, concurrent, and adaptive multiscale strategies, including their advantages and limitations.
  • Identification of limited but growing applications of concurrent methods in polymer science.

Conclusions:

  • Multiscale modeling is a rapidly evolving field with diverse possibilities and challenges for polymeric materials.
  • Further research is needed to fully exploit concurrent and adaptive methods in polymer science.
  • Novel ideas for extending atomistic techniques and addressing existing challenges are outlined for future research.