arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.11505cond-mat.mes-hallphysics.comp-ph
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从高分辨率透射电子显微镜图像确定碳纳米管手性指数的深度学习方法

A deep learning approach for determining the chiral indices of carbon nanotubes from high-resolution transmission electron microscopy images

G. D. Förster, A. Castan, A. Loiseau, J. Nelayah, D. Alloyeau, F. Fossard, C. Bichara, H. Amara

摘要:

手性指数决定了碳纳米管的重要性质。不幸的是,从高分辨率透射电子显微镜(HRTEM)图像,最准确的方法分配手性,他们的决心是一项繁琐的任务。我们开发了一个卷积神经网络来自动化这个过程。一个大的和现实的CNT图像的训练数据集,通过原子的计算机模拟,再加上图像生成的多切片方法。在大多数情况下,自动分配的结果与手动分类非常一致,并确定了故障的起源。目前的方法结合了HRTEM成像和深度学习算法,可以分析统计上大量的碳纳米管HRTEM图像,为实验手性分布的稳健估计铺平了道路。

英文摘要:

Chiral indices determine important properties of carbon nanotubes (CNTs). Unfortunately, their determination from high-resolution transmission electron microscopy (HRTEM) images, the most accurate method for assigning chirality, is a tedious task. We develop a Convolutional Neural Network that automatizes this process. A large and realistic training data set of CNT images is obtained by means of atomistic computer simulations coupled with the multi-slice approach for image generation. In most cases, results of the automated assignment are in excellent agreement with manual classification, and the origin of failures is identified. The current approach, which combines HRTEM imaging and deep learning algorithms allows the analysis of a statistically significant number of HRTEM images of carbon nanotubes, paving the way for robust estimates of experimental chiral distributions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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