An efficient self-avoiding random walk algorithm to generate large-scale polymer and polymer nanocomposite samples at molecular resolution - dataset

Abstract:from [1] Polymers are highly versatile materials that can be enhanced further by adding nano-sized filler particles to meet the requirements of high-performance applications. To this end, coarse-grained molecular dynamics (CGMD) simulations are used to unravel the complex structure-property relation of polymer nanocomposites (PNC). While a wide range of established software is available for CGMD, creating PNC samples with the necessary flexibility, i.e., nanofiller shape and positioning, poses a challenge. To address this, we introduce a novel self-avoiding random walk (SARW) algorithm. This algorithm offers a wide range of functionalities that allow users to adjust the geometry of the simulation box and the polymer chains, including bond lengths, angle constraints, and dispersity. Additionally, the SARW provides various options for customizing the shape, size, orientation, number, and positioning of nanoparticles. It effectively incorporates colloids, fibers, and platelets into the polymer matrix. The SARW is highly efficient, capable of generating systems with over 50 million beads in minutes, and is designed to be user-friendly and easily extensible. We showcase the SARW's features through practical examples and publish the associated code as open-source. Hence, this work paves the way for large-scale molecular dynamics studies on polymer melts and polymer nanocomposites, helping to unlock their full potential. Contact: Maximilian RiesInstitute of Applied MechanicsFriedrich-Alexander-Universität Erlangen-NürnbergEgerlandstr. 591058 Erlangen Software: All computations were performed with Julia (https://julialang.org/) License: Creative Commons Attribution 4.0 International Context: Data set supplementing journal paper:[1] J. Roksvaag M. Ries, "An efficient self-avoiding random walk algorithm to generate large-scale polymer and polymer nanocomposite samples at molecular resolution" Molecular Simulation (2026). Content: scripts to run the examples visualized in the article Installation in julia terminal:using Pkg; Pkg.add(url=“https://github.com/Joroks/SelfAvoidingRandomWalker.jl”) Run examples in julia terminal:include("<example name>.jl")