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Amoeba Monte Carlo algorithms for random trees with controlled branching activity: Efficient trial move generation
Pieter H W van der Hoek1, Angelo Rosa1, Ralf Everaers2
1SISSA - <a href="https://ror.org/004fze387">Scuola Internazionale Superiore di Studi Avanzati</a>, Via Bonomea 265, 34136 Trieste, Italy.
Physical Review. E
|November 20, 2024
Summary
We introduce two new Amoeba algorithms for simulating branched polymers, improving efficiency in polymer equilibration. These algorithms reveal a novel scaling regime for polymer relaxation dynamics.
Area of Science:
- Polymer Physics
- Computational Chemistry
- Statistical Mechanics
Background:
- The reptation Monte Carlo algorithm is effective for linear polymer equilibration.
- Simulating branched polymers presents unique computational challenges.
Purpose of the Study:
- To develop efficient simulation methods for randomly branching polymer chains.
- To analyze the relaxation dynamics and equilibration times of these branched polymer systems.
Main Methods:
- Generalization of the Amoeba algorithm for randomly branching chains.
- Analysis of relaxation dynamics using Monte Carlo simulations.
- Identification of scaling regimes in polymer equilibration.
Main Results:
- Proposed two generalized Amoeba algorithms for efficient simulation of branched polymers.
- Demonstrated an unexpected scaling regime in the relaxation dynamics of polymer trees.
- Derived a scaling law for equilibration time: N^2〈n_lin〉^Δ, with Δ≃0.4.
Conclusions:
- The generalized Amoeba algorithms offer efficient equilibration for branched polymer solutions.
- The identified scaling regime provides new insights into polymer dynamics and computational modeling.
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