arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2006.15895cond-mat.softcond-mat.dis-nncond-mat.mtrl-sciphysics.chem-ph
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通过卷积神经网络从离子表面活性剂到Nafion

From ionic surfactants to Nafion through convolutional neural networks

Loic Dumortier, Stefano Mossa

摘要:

我们已经将最新的机器学习进展,深度卷积神经网络,应用于三维(体素)软物质数据,由分子动力学计算机模拟生成。我们集中在结构和相的性质的粗粒模型的水合离子表面活性剂。我们已经训练了一个分类器,能够自动检测系统中吸收的水量,因此将每个水合水平与相应的最具代表性的纳米结构相关联。基于迁移学习的概念,我们接下来将相同的网络应用于相关的聚合离聚物Nafion,并提取了这些配置与上述配置的相似性的度量。我们证明,在此基础上,它是可能的表示在固定的水合水平的聚合物的静态结构因子作为叠加的表面活性剂在多个水含量。我们认为,这样的程序可以提供一个有用的,不可知的,数据驱动的,精确的描述无序材料的多尺度结构,而不诉诸任何先验模型图片。

英文摘要:

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.

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