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.

翻译：本研究探索了一种从单体出发引发高交联材料聚合的通用方法。随着聚合反应的进行，由于新化学键的形成，力场参数会持续发生变化。这一动态过程不仅直接影响参与成键的原子，还会改变其邻近的原子环境。监测高交联结构中这些复杂的变化颇具挑战。为解决这一问题，我们引入了一种基于图网络的算法，该算法能够实现快速而准确的预测。该算法将聚合物构建协议与大规模分子动力学模拟软件LAMMPS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（包括多孔聚合物网络（PPNs）和环氧树脂）得到验证，同时该算法还被用于其他任务，例如实现孔隙贯穿变形和计算材料性能。