• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Nanotechnology >Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data
【24h】

Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data

机译：通过机器学习X射线衍射显微镜数据成像纳米级晶格变化

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We present a novel methodology based on machine learning to extract lattice variations in crystalline materials, at the nanoscale, from an x-ray Bragg diffraction-based imaging technique. By employing a full-field microscopy setup, we capture real space images of materials, with imaging contrast determined solely by the x-ray diffracted signal. The data sets that emanate from this imaging technique are a hybrid of real space information (image spatial support) and reciprocal lattice space information (image contrast), and are intrinsically multidimensional (5D). By a judicious application of established unsupervised machine learning techniques and multivariate analysis to this multidimensional data cube, we show how to extract features that can be ascribed physical interpretations in terms of common structural distortions, such as lattice tilts and dislocation arrays. We demonstrate this 'big data' approach to x-ray diffraction microscopy by identifying structural defects present in an epitaxial ferroelectric thin-film of lead zirconate titanate.
机译：我们提出了一种基于机器学习的新颖方法，该方法可从基于X射线布拉格衍射的成像技术中提取纳米级的晶体材料中的晶格变异。通过采用全视野显微镜设置，我们可以捕获材料的真实空间图像，而成像对比度仅由X射线衍射信号确定。从这种成像技术中产生的数据集是真实空间信息（图像空间支持）和倒数晶格空间信息（图像对比度）的混合，本质上是多维（5D）。通过将成熟的无监督机器学习技术和多变量分析明智地应用到此多维数据立方体，我们展示了如何根据常见的结构变形（如晶格倾斜和位错阵列）提取可以归因于物理解释的特征。我们通过识别钛酸锆钛酸铅外延铁电薄膜中存在的结构缺陷，证明了这种“大数据”方法在X射线衍射显微镜中的应用。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号