论文标题
从离子表面活性剂到Nafion通过卷积神经网络
From ionic surfactants to Nafion through convolutional neural networks
论文作者
论文摘要
我们将最近的机器学习进展（深度卷积神经网络）应用于三维（体素）软件数据，该数据由分子动力学计算机模拟生成。我们专注于水合离子表面活性剂的粗粒模型的结构和相性质。我们已经训练了一个能够自动检测系统吸收的水量的分类器，因此将水量与每个水合水平相关联，相应的最具代表性的纳米结构。基于转移学习的概念，我们接下来将相同的网络应用于相关的聚合物离子Nafion，并提取了这些配置与上述这些配置的相似性的度量。我们证明，在此基础上，有可能在固定水合水平上表达聚合物的静态结构因子，作为在多种水含量下表面活性剂的叠加。我们建议这样的过程可以提供有用的，不可知的，数据驱动的，精确的材料多尺度结构的描述，而无需诉诸于任何A-Priori模型图片。
We have applied recent machine learning advances, deep convolutional neural network, to three-dimensional (voxels) soft matter data, generated by Molecular Dynamics computer simulation. We have focused on the structural and phase properties of a coarse-grained model of hydrated ionic surfactants. We have trained a classifier able to automatically detect the water quantity absorbed in the system, therefore associating to each hydration level the corresponding most representative nano-structure. Based on the notion of transfer learning, we have next applied the same network to the related polymeric ionomer Nafion, and have extracted a measure of the similarity of these configurations with those above. We demonstrate that on this basis it is possible to express the static structure factor of the polymer at fixed hydration level as a superposition of those of the surfactants at multiple water contents. We suggest that such a procedure can provide a useful, agnostic, data-driven, precise description of the multi-scale structure of disordered materials, without resorting to any a-priori model picture.
