Methods for Creation and Linear Elastic Response Analysis of Packings of Semi-flexible Soft Polymer Chains

Abstract

In this methods paper, I show how one can find and study the physical properties of packings of flexible chains, rigid molecules, and everything in between. In addition to describing the energy landscape of these materials, these methods describe how to shear stabilize polymer packings, classify them in the jamming hierarchy, describe their elastic properties, and much more. This simple modification to soft sphere simulations has the potential to yield new discoveries surrounding glasses. :contentReference[oaicite:0]{index=0}

Publication Details

  • Author: R. Cameron Dennis
  • Journal: arXiv Preprint
  • ArXiv ID: 2110.07793
  • Publication Date: March 14, 2024
  • DOI: 10.48550/arXiv.2110.07793

Key Findings

  • Extension of Soft Sphere Models: Introduced a methodology to extend traditional soft sphere simulations to model packings of semi-flexible polymer chains, bridging the gap between flexible chains and rigid molecules.
  • Shear Stabilization and Jamming Classification: Developed techniques to shear stabilize polymer packings and classify them within the jamming hierarchy, enhancing the understanding of their mechanical stability.
  • Elastic Property Analysis: Provided methods to analyze the elastic properties of these polymer packings, offering insights into their mechanical responses under various conditions.
  • Implications for Glassy Systems: Suggested that this approach could lead to new discoveries in the study of glasses by allowing the simulation of more complex molecular structures beyond simple soft spheres.

Relevant Figures

Figure 1: Visualization of Semi-flexible Polymer Chain Packing

Figure 1: Visualization of Semi-flexible Polymer Chain Packing

Associated Project

This publication is associated with the Polymer Chain Packings project.

R. Cameron Dennis, Ph.D.
R. Cameron Dennis, Ph.D.
Physicist | Quantitative Researcher | Data Scientist

Physicist specializing in quantitative modeling, machine learning, and complex systems. Passionate about bridging research with real-world applications.