Weijian Zheng, Jun-Sang Park, Peter Kenesei, Ahsan Ali, Zhengchun Liu, Ian Foster, Nicholas Schwarz, Rajkumar Kettimuthu, Antonino Miceli, Hemant Sharma
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引用次数: 0
摘要
高能 X 射线衍射方法可以非破坏性地绘制金属多晶工程材料的三维微观结构和相关属性。这些方法通常与热机械加载等外部刺激相结合,以拍摄随时间演变的微观结构和属性的快照。然而,传统的数据采集和还原方法数据量巨大、成本高昂,阻碍了快速提取可行见解和提高这些快照的时间分辨率。本文介绍了一种能够在高能 X 射线显微镜数据中快速检测可塑性开始的全自动技术。该技术的计算速度比传统方法至少快 50 倍,而且适用于比完整数据集稀疏 9 倍的数据集。这项新技术利用自监督图像表征学习和聚类,将海量数据集转化为紧凑、语义丰富的视觉显著特征(如峰值形状)表征。这些特征可迅速显示异常事件,如衍射峰形状的变化。预计这项技术将提供及时可操作的信息,以推动更智能的实验,有效部署跨越数十年长度尺度的多模态 X 射线衍射方法。
Rapid detection of rare events from in situX-ray diffraction data using machine learning.
High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics (e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.
期刊介绍:
Many research topics in condensed matter research, materials science and the life sciences make use of crystallographic methods to study crystalline and non-crystalline matter with neutrons, X-rays and electrons. Articles published in the Journal of Applied Crystallography focus on these methods and their use in identifying structural and diffusion-controlled phase transformations, structure-property relationships, structural changes of defects, interfaces and surfaces, etc. Developments of instrumentation and crystallographic apparatus, theory and interpretation, numerical analysis and other related subjects are also covered. The journal is the primary place where crystallographic computer program information is published.