期刊
IUCRJ
卷 9, 期 -, 页码 104-113出版社
INT UNION CRYSTALLOGRAPHY
DOI: 10.1107/S2052252521011258
关键词
materials science; high-pressure powder diffraction; WAXS; X-ray microscopy; structure prediction
资金
- US Department of Energy, Office of Science
- Office of Basic Energy Sciences [FWP-35896, DE-AC0206CH11357]
The deep-learning based method BraggNN can rapidly determine diffraction peak positions with higher accuracy compared to traditional pseudo-Voigt fitting. Experimental results show significant improvements in accuracy and computational speed when using BraggNN, showcasing its potential for real-time applications in X-ray diffraction microscopy techniques.
X-ray diffraction based microscopy techniques such as high-energy diffraction microscopy (HEDM) rely on knowledge of the position of diffraction peaks with high precision. These positions are typically computed by fitting the observed intensities in detector data to a theoretical peak shape such as pseudo-Voigt. As experiments become more complex and detector technologies evolve, the computational cost of such peak-shape fitting becomes the biggest hurdle to the rapid analysis required for real-time feedback in experiments. To this end, we propose BraggNN, a deep-learning based method that can determine peak positions much more rapidly than conventional pseudo-Voigt peak fitting. When applied to a test dataset, peak center-of-mass positions obtained from BraggNN deviate less than 0.29 and 0.57 pixels for 75 and 95% of the peaks, respectively, from positions obtained using conventional pseudo-Voigt fitting (Euclidean distance). When applied to a real experimental dataset and using grain positions from near-field HEDM reconstruction as ground-truth, grain positions using BraggNN result in 15% smaller errors compared with those calculated using pseudo-Voigt. Recent advances in deep-learning method implementations and special-purpose model inference accelerators allow BraggNN to deliver enormous performance improvements relative to the conventional method, running, for example, more than 200 times faster on a consumer-class GPU card with out-of-the-box software.
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