A deep convolutional neural network for real-time full profile analysis of big powder diffraction data
A deep convolutional neural network for real-time full profile analysis of big powder diffraction data
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DOI：
10.1038/s41524-021-00542-4
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发表时间：
2021-05-21
影响因子：
9.7
通讯作者：
Vamvakeros, Antonis
中科院分区：
文献类型：
--
作者：
Dong, Hongyang;Butler, Keith T.;Vamvakeros, Antonis
我们提出了参数量化网络（PQ-Net），这是一种回归深度卷积神经网络，可对多相系统的粉末X射线衍射图谱进行定量分析。该网络进行了测试，对模拟和实验数据集的复杂性不断增加，最后一个是一个X射线衍射计算机断层扫描数据集的多相Ni-Pd/CeO 2-ZrO 2/Al 2 O3催化材料系统组成的ca。两万个衍射图案。结果表明，该网络预测准确的比例因子，晶格参数和微晶尺寸图的所有阶段，这是通过全剖面分析使用Rietveld方法获得的，也提供了一个可靠的不确定性措施的结果。PQ-Net的主要优点是能够更快地产生这些结果，显示出其作为原位/操作实验期间实时衍射数据分析工具的潜力。
We present Parameter Quantification Network (PQ-Net), a regression deep convolutional neural network providing quantitative analysis of powder X-ray diffraction patterns from multi-phase systems. The network is tested against simulated and experimental datasets of increasing complexity with the last one being an X-ray diffraction computed tomography dataset of a multi-phase Ni-Pd/CeO2-ZrO2/Al2O3 catalytic material system consisting of ca. 20,000 diffraction patterns. It is shown that the network predicts accurate scale factor, lattice parameter and crystallite size maps for all phases, which are comparable to those obtained through full profile analysis using the Rietveld method, also providing a reliable uncertainty measure on the results. The main advantage of PQ-Net is its ability to yield these results orders of magnitude faster showing its potential as a tool for real-time diffraction data analysis during in situ/operando experiments.