Abstract：
Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable for advancing the design principles of final products at reduced processability costs. While ab initio molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers, such as dipole-dipole interactions and hydrogen bonding, and analyzing their influence on the molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIPs) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures and properties at large scales (polymers). NNIP provides critical insight into intra- and interchain hydrogen-bonding and dipolar correlations and accurately predicts the amorphous bulk PAN structure validated by modeling the experimental X-ray structure factor. Furthermore, the NNIP-predicted PAN properties, such as density and elastic modulus, are in good agreement with their experimental values. Overall, the trend in the elastic modulus is found to correlate strongly with the PAN structural orientations encoded in the Hermans orientation factor. This study enables the ability to predict the structure-property relations for PAN and analogues with sustainable ab initio accuracy across scales.
摘要：
聚丙烯腈(PAN)是一种重要的商用聚合物,具有非选择性自由基聚合产生的无规立构立体化学。因此，一个准确的，对控制PAN分子单元之间相互作用的基本理解对于以降低的加工成本推进最终产品的设计原则是必不可少的。虽然从头算分子动力学（AIMD）模拟可以为处理极性聚合物中的关键相互作用提供必要的准确性，如偶极-偶极相互作用和氢键，分析它们对分子取向的影响，它们的实现仅限于小分子。在这里，我们表明，在小规模AIMD数据（低聚物获得）上训练的神经网络原子间势（NNIP）可以有效地用于检查大规模（聚合物）的结构和性质。NNIP提供了对链内和链间氢键键合和偶极相关性的关键见解，并通过对实验X射线结构因子进行建模来准确预测无定形块状PAN结构。此外，NNIP预测的PAN属性，如密度和弹性模量，与他们的实验值非常吻合。总的来说,发现弹性模量的趋势与Hermans取向因子中编码的PAN结构取向密切相关。这项研究能够预测PAN和类似物的结构-性质关系，并具有可持续的从头算准确性。
