Neural network model for structure factor of polymer systems
Neural network model for structure factor of polymer systems
复制标题
聚合物体系结构因子的神经网络模型
DOI：
10.1063/5.0022464
复制
发表时间：
2020-09-28
影响因子：
4.4
通讯作者：
Huang, Gang
中科院分区：
文献类型：
--
作者：
Huang, Jie;Li, Shiben;Huang, Gang
作为理解聚合物链内部结构的重要物理量，结构因子正在理论和实验上得到研究。理论上，高斯链的结构因子已经通过解析求解，但对于蠕虫链，通常采用数值方法，例如蒙特卡罗模拟，求解修正的扩散方程。在这些工作中，需要针对不同区域的波矢和链条刚度计算不同的结构因子，并且某些计算过程非常消耗资源。在这项工作中，通过训练深度神经网络，我们获得了一个有效的模型来计算聚合物链的结构因子，而无需考虑不同区域的波数和链刚性。此外，基于训练好的神经网络模型，我们利用散射实验数据预测了一些聚合物链的轮廓和库恩长度，我们发现我们的模型可以获得相当合理的预测。这项工作提供了一种获得聚合物链结构因子的方法，该方法与之前的方法一样好，并且计算效率更高。它还为实验研究人员测量聚合物链的轮廓和库恩长度提供了一种潜在的方法。
As an important physical quantity to understand the internal structure of polymer chains, the structure factor is being studied both in theory and experiment. Theoretically, the structure factor of Gaussian chains has been solved analytically, but for wormlike chains, numerical approaches are often used, such as Monte Carlo simulations, solving the modified diffusion equation. In these works, the structure factor needs to be calculated differently for different regions of the wave vector and chain rigidity, and some calculation processes are resource consuming. In this work, by training a deep neural network, we obtained an efficient model to calculate the structure factor of polymer chains, without considering different regions of wavenumber and chain rigidity. Furthermore, based on the trained neural network model, we predicted the contour and Kuhn lengths of some polymer chains by using scattering experimental data, and we found that our model can get pretty reasonable predictions. This work provides a method to obtain the structure factor for polymer chains, which is as good as previous and more computationally efficient. It also provides a potential way for the experimental researchers to measure the contour and Kuhn lengths of polymer chains.