论文标题
一种深度学习方法，用于确定高分辨率传输电子显微镜图像的碳纳米管的手性指数
A deep learning approach for determining the chiral indices of carbon nanotubes from high-resolution transmission electron microscopy images
论文作者
论文摘要
手性指数确定碳纳米管（CNT）的重要特性。不幸的是，它们从高分辨率透射电子显微镜（HRTEM）图像（分配手性的最准确方法）中的确定是一项繁琐的任务。我们开发一个自动化此过程的卷积神经网络。 CNT图像的大型训练数据集是通过原子计算机模拟以及用于图像生成的多层方法获得的。在大多数情况下，自动化分配的结果与手动分类非常一致，并且确定了失败的起源。当前的方法结合了HRTEM成像和深度学习算法，可以分析碳纳米管的统计学数量大量的HRTEM图像，为实验性手性分布的强大估计铺平了道路。
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.
