Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States.
Department of Chemical Engineering, University of California, Davis, California 95616, United States.
J Chem Theory Comput. 2021 Jan 12;17(1):463-473. doi: 10.1021/acs.jctc.0c00967. Epub 2020 Dec 3.
Initial atomistic-level radiation damage in chemically reactive materials is thought to induce reaction cascades that can result in undesirable degradation of macroscale properties. Ensembles of quantum-based molecular dynamics (QMD) simulations can accurately predict these cascades, but extracting chemical insights from the many underlying trajectories is a labor-intensive process that can require substantial intuition. We develop here a general and automated graph-based approach to extract all chemically distinct structures sampled in QMD simulations and apply our approach to predict primary radiation damage of polydimethylsiloxane (PDMS), the main constituent of silicones. A postprocessing protocol is developed to identify underlying polymer backbone structures as connected components in QMD trajectories. These backbones form a repository of radiation-damaged structures. A scheme for extracting and updating a library of isomorphically distinct structures is proposed to identify the spanning set and aid chemical interpretation of the repository. The analyses are applied to ensembles of cascade QMD simulations in which the four element types in PDMS are selectively excited in primary knock-on atom events. Our approach reveals a much higher degree of combinatorial complexity in this system than was inferred through radiolysis experiments. Probabilities are extracted for radiation-induced network changes including formation of branch points, carbon linkages, cycles, bond scissions, and carbon uptake into the Si-O siloxane backbone network. The general analysis framework presented here is readily extendable to modeling chemical degradation of other polymers and molecular materials and provides a basis for future quantum-informed multiscale modeling of radiation damage.
在化学反应性材料中，初始原子级别的辐射损伤被认为会引发反应级联，从而导致宏观性质的不良降解。基于量子的分子动力学（QMD）模拟的集合可以准确地预测这些级联，但从许多潜在轨迹中提取化学见解是一个劳动密集型过程，可能需要大量的直觉。我们在这里开发了一种通用的自动化基于图的方法，用于提取 QMD 模拟中采样的所有化学上不同的结构，并将我们的方法应用于预测聚二甲基硅氧烷（PDMS）的初级辐射损伤，PDMS 是硅酮的主要成分。开发了一种后处理协议来识别 QMD 轨迹中的聚合物主链结构作为连通组件。这些骨干形成了一个辐射损伤结构的存储库。提出了一种提取和更新同构不同结构库的方案，以确定跨越集并帮助对存储库进行化学解释。该分析应用于级联 QMD 模拟的集合中，其中 PDMS 中的四种元素类型在初级撞击原子事件中被选择性激发。我们的方法揭示了该系统比通过放射分解实验推断的组合复杂度更高。提取了辐射诱导的网络变化的概率，包括分支点、碳键合、循环、键断裂和碳摄取到 Si-O 硅氧烷主链网络中。这里提出的通用分析框架很容易扩展到其他聚合物和分子材料的化学降解建模，并为未来基于量子的辐射损伤多尺度建模提供了基础。
