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Simulating dynamic bonding in soft materials

Tyla R. Holoman, B. P. Prajwal, Glen M. Hocky, Thomas M. Truskett

Abstract

Dynamic bonding is an essential feature of many soft materials. Molecular simulations have proven to be a powerful tool for modeling bonding kinetics and thermodynamics in these materials, providing insights into their properties that cannot be obtained by experiments alone. Here, we review recent advances in modeling dynamic bonding in soft matter via molecular dynamics, Monte Carlo, and hybrid simulation methods, highlighting outstanding challenges and future directions.

Simulating dynamic bonding in soft materials

Abstract

Dynamic bonding is an essential feature of many soft materials. Molecular simulations have proven to be a powerful tool for modeling bonding kinetics and thermodynamics in these materials, providing insights into their properties that cannot be obtained by experiments alone. Here, we review recent advances in modeling dynamic bonding in soft matter via molecular dynamics, Monte Carlo, and hybrid simulation methods, highlighting outstanding challenges and future directions.

Paper Structure

This paper contains 12 sections, 8 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Molecular dynamics
  3. Monte Carlo
  4. Hybrid MD/MC
  5. Modeling challenges and outlook
  6. Declaration of Competing Interest
  7. Acknowledgment

Figures (3)

  • Figure 1: Mechanisms associated with network formation and reversible rearrangement using star polymers as building blocks. a) Reversible dissociative bonding process between two types of star polymers, A (green) and B (red). b) Linker-mediated bonding of star polymers. c) Reversible associative bonding for a linker-mediated star polymer network.
  • Figure 2: Examples of simulated dynamic soft matter. a) Self-healing vitrimers (reproduced from Ciarella2019Swap-DrivenVitrimers). b) Polymer-grafted nanoparticles with bond exchange reactions (reproduced from Chen2024TopologicalExchanging). c) Actomyosin networks, simulated with AFINES (adapted from Freedman2018NonequilibriumNetworks). d) Cytoskeletal assembly, modeled using kinetic Monte Carlo simulations (reproduced from Yan2022TowardAssemblies).
  • Figure 3: Comparison of Monte Carlo algorithms for modeling a) dissociative (reproduced from Blanco2024TheMethod) and b) associative (reproduced from Rao2024APolymers) interactions.