基于图网络的高交联聚合物体系预测建模
Graph-Network-Based Predictive Modeling for Highly Cross-Linked Polymer Systems
AI总结：
本文提出一种基于图网络的算法，将聚合物构建流程与LAMMPS结合，用于快速准确预测高交联聚合物在聚合过程中的力场变化，并成功应用于多孔聚合物网络和环氧树脂等体系。
摘要：
本研究探讨了一种在高交联材料中从单体引发聚合的通用方法。随着聚合的进行，由于新化学键的形成，力场参数会发生连续变化。这一动态过程不仅影响直接参与成键的原子，还会影响邻近的原子环境。监测高交联结构中这些复杂变化是一项挑战。为解决这一问题，我们提出了一种基于图网络的算法，该算法能够提供快速且准确的预测。该算法将聚合物构建流程与LAMMPS（一种大规模分子动力学模拟软件）相结合。该代码的适应性已通过其成功应用于多种无定形聚合物得到验证，包括多孔聚合物网络（PPNs）和环氧树脂，同时该算法还被用于其他任务，例如实现穿孔变形和计算材料性能。
英文摘要：
In this study, a versatile methodology for initiating polymerization from monomers in highly cross-linked materials is investigated. As polymerization progresses, force-field parameters undergo continuous modification due to the formation of new chemical bonds. This dynamic process not only impacts the atoms directly involved in bonding, but also influences the neighboring atomic environment. Monitoring these complex changes in highly cross-linked structures poses a challenge. To address this issue, we introduce a graph-network-based algorithm that offers both rapid and accurate predictions. The algorithm merges polymer construction protocols with LAMMPS, a large-scale molecular dynamics simulation software. The adaptability of this code has been demonstrated by its successful application to various amorphous polymers, including porous polymer networks (PPNs), and epoxy-resins, while the algorithm has been employed for additional tasks, such as implementing pore-piercing deformations and calculating material properties.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
