动态键合软材料的模拟与网络组装流程
Simulation and Network Assembly Pipelines for Dynamically Bonded Soft Materials
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中文总结 AI 辅助
本文提出pySNAP，一个基于GPU加速的模块化开源Python平台，集成可调动态键合与工作流，用于模拟动态键合软材料，支持多种键合机制，可复现现有模型并构建复杂复合系统。
中文摘要 AI 辅助
由可逆共价键或超分子键连接的软材料形成了一类多样化的组装体，在从纳米科学到医学等领域具有广阔的应用前景。实验通常探测体相行为和流变学，但难以测量微观键合动力学和组成元素的力学如何产生体相性质。粗粒化分子动力学（MD）模拟可以桥接这些尺度，但大多数模拟方法不控制单个键的动力学，而那些控制的方法大多是为不能普遍推广的定制应用而开发的。在此，我们提出pySNAP（模拟与网络组装流程），一个模块化的开源Python平台，它将可调动态键合与工作流、模板和分析设置集成在一起，使用户只需对输入文件进行少量更改即可研究各种系统。该平台基于GPU加速的HOOMD-blue MD引擎构建，并集成了DyBond，一个GPU加速的插件，该插件以与平衡分布一致的方式形成和断裂键，并支持多种类型的伙伴物种之间的键合。围绕这一核心，snap_simulate包将参数文件目录编译成HOOMD-blue模拟，snap_workflow包则在工作站和高性能计算调度器上编排由此产生的参数扫描。我们描述了模拟动态键合背后的理论以及如何使用该包，从设置参数扫描到分析其结果，并在各种动态键合系统上展示了该框架，表明它在一个统一的工作流中容纳了不同的相互作用机制、几何形状和物理场景，同时既能复现现有模型，又能快速构建更复杂的复合系统。
英文摘要
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.
发表机构
- McKetta Department of Chemical Engineering, University of Texas at Austin（德克萨斯大学奥斯汀分校麦凯塔化学工程系）
- Department of Chemistry, New York University（纽约大学化学系）
- Simons Center for Computational Physical Chemistry, New York University（纽约大学西蒙斯计算物理化学中心）
- Department of Biomedical Engineering and Center for Biomolecular Condensates, Washington University in St. Louis（华盛顿大学圣路易斯分校生物医学工程系和生物分子凝聚体中心）
- Department of Chemical Engineering and Biointerfaces Institute, University of Michigan（密歇根大学化学工程系和生物界面研究所）
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