Despite growing interest in polymers under extreme conditions, most atomistic molecular dynamics simulations cannot describe the bond scission events underlying failure modes in polymer networks undergoing large strains. In this work, we propose a physics-based machine learning approach that can detect and perform bond breaking with near quantum-chemical accuracy on-the-fly in atomistic simulations. Particularly, we demonstrate that by coarse-graining highly correlated neighboring bonds, the prediction accuracy can be dramatically improved. By comparing with existing quantum mechanics/molecular mechanics methods, our approach is approximately two orders of magnitude more efficient and exhibits improved sensitivity toward rare bond breaking events at low strain. The proposed bond breaking molecular dynamics scheme enables fast and accurate modeling of strain hardening and material failure in polymer networks and can accelerate the design of polymeric materials under extreme conditions.

中文翻译：

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极端变形下热固性材料中的机器学习量子化学键断裂

尽管人们对极端条件下的聚合物越来越感兴趣，但大多数原子分子动力学模拟无法描述承受大应变的聚合物网络中失效模式下的断键事件。在这项工作中，我们提出了一种基于物理学的机器学习方法，该方法可以在原子模拟中以接近量子化学的精度即时检测和执行键断裂。特别是，我们证明通过粗粒度高度相关的相邻键，可以显着提高预测精度。通过与现有的量子力学/分子力学方法相比，我们的方法效率提高了大约两个数量级，并且对低应变下罕见的键断裂事件表现出更高的敏感性。