Roksvaag J, Ries M (2026)
Publication Type: Journal article
Publication year: 2026
DOI: 10.1080/08927022.2026.2672414
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 customising the shape, size, orientation, number, and positioning of nanoparticles. It effectively incorporates colloids, fibres, 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.
APA:
Roksvaag, J., & Ries, M. (2026). An efficient self-avoiding random walk algorithm to generate large-scale polymer and polymer nanocomposite samples at molecular resolution. Molecular Simulation. https://doi.org/10.1080/08927022.2026.2672414
MLA:
Roksvaag, Johannes, and Maximilian Ries. "An efficient self-avoiding random walk algorithm to generate large-scale polymer and polymer nanocomposite samples at molecular resolution." Molecular Simulation (2026).
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