Simulation and Network Assembly Pipelines for Dynamically Bonded Soft Materials
Abstract
Soft materials linked by reversible covalent or supramolecular bonds form a diverse class of assemblies with promising applications from nanoscience to medicine. Experiments typically probe bulk phase behavior and rheology, but it remains difficult to measure how microscopic bonding kinetics and the mechanics of the constituent elements give rise to bulk properties. Coarse-grained molecular dynamics (MD) simulations can bridge these scales, but most simulation approaches do not control individual bond kinetics, and those that do were mostly developed for bespoke applications that do not readily generalize. Here we present pySNAP (Simulation and Network Assembly Pipelines), a modular open source Python platform that integrates tunable dynamic bonding with a workflow, template, and analysis setup, so that users can study a wide range of systems with only small changes to input files. The platform is built on the GPU-accelerated HOOMD-blue MD engine and integrates DyBond, a GPU-accelerated plugin that forms and breaks bonds consistent with an equilibrium distribution and supports bonding between multiple types of partner species. Around this core, the snap_simulate package compiles a directory of parameter files into a HOOMD-blue simulation, and the snap_workflow package orchestrates the resulting parameter sweeps across workstations and high-performance computing schedulers. We describe the theory behind simulated dynamic bonding and how to use the package, from setting up a parameter sweep to analyzing its results, and demonstrate the framework on a diverse range of dynamically bonded systems, showing that it accommodates distinct interaction mechanisms, geometries, and physical scenarios within a unified workflow, while enabling both reproduction of existing models and rapid construction of more complex composite systems.
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BibTeX
@misc{wilcoxson2026simulation,
title = {{Simulation and Network Assembly Pipelines for Dynamically Bonded Soft Materials}},
author = {Tanner A. Wilcoxson and Tianhao Li and Tyla R. Holoman and Gaurav Mitra and Thomas M. Truskett and Glen M. Hocky},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.21204},
eprint = {2609.21204},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.21204},
}
RIS
TY - UNPB
TI - Simulation and Network Assembly Pipelines for Dynamically Bonded Soft Materials
AU - Tanner A. Wilcoxson
AU - Tianhao Li
AU - Tyla R. Holoman
AU - Gaurav Mitra
AU - Thomas M. Truskett
AU - Glen M. Hocky
PY - 2026
DA - 2026/09/18
PB - arXiv
DO - 10.48550/arxiv.2609.21204
UR - https://arxiv.org/abs/2609.21204
AB - Soft materials linked by reversible covalent or supramolecular bonds form a diverse class of assemblies with promising applications from nanoscience to medicine. Experiments typically probe bulk phase behavior and rheology, but it remains difficult to measure how microscopic bonding kinetics and the mechanics of the constituent elements give rise to bulk properties. Coarse-grained molecular dynamics (MD) simulations can bridge these scales, but most simulation approaches do not control individual bond kinetics, and those that do were mostly developed for bespoke applications that do not readily generalize. Here we present pySNAP (Simulation and Network Assembly Pipelines), a modular open source Python platform that integrates tunable dynamic bonding with a workflow, template, and analysis setup, so that users can study a wide range of systems with only small changes to input files. The platform is built on the GPU-accelerated HOOMD-blue MD engine and integrates DyBond, a GPU-accelerated plugin that forms and breaks bonds consistent with an equilibrium distribution and supports bonding between multiple types of partner species. Around this core, the snap_simulate package compiles a directory of parameter files into a HOOMD-blue simulation, and the snap_workflow package orchestrates the resulting parameter sweeps across workstations and high-performance computing schedulers. We describe the theory behind simulated dynamic bonding and how to use the package, from setting up a parameter sweep to analyzing its results, and demonstrate the framework on a diverse range of dynamically bonded systems, showing that it accommodates distinct interaction mechanisms, geometries, and physical scenarios within a unified workflow, while enabling both reproduction of existing models and rapid construction of more complex composite systems.
ER -