scholarly journals A deep convolutional neural network for real-time full profile analysis of big powder diffraction data

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Hongyang Dong ◽  
Keith T. Butler ◽  
Dorota Matras ◽  
Stephen W. T. Price ◽  
Yaroslav Odarchenko ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Diffraction Data ◽  
Profile Analysis ◽  
X Ray Diffraction ◽  
X Ray ◽  
Full Profile ◽  
Diffraction Patterns ◽  
Multi Phase

AbstractWe 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.

Crystal symmetry classification from powder X-ray diffraction patterns using a convolutional neural network

2020 ◽  
Vol 25 ◽  
pp. 101662
Author(s):  
Alexander N. Zaloga ◽  
Vladimir V. Stanovov ◽  
Oksana E. Bezrukova ◽  
Petr S. Dubinin ◽  
Igor S. Yakimov
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crystal Symmetry ◽  
X Ray Diffraction ◽  
X Ray ◽  
Symmetry Classification ◽  
Diffraction Patterns

IDATENandG-SITENNO: GUI-assisted software for coherent X-ray diffraction imaging experiments and data analyses at SACLA

2014 ◽  
Vol 21 (6) ◽  
pp. 1378-1383 ◽  
Author(s):  
Yuki Sekiguchi ◽  
Masaki Yamamoto ◽  
Tomotaka Oroguchi ◽  
Yuki Takayama ◽  
Shigeyuki Suzuki ◽  
...  
Keyword(s):  
User Interfaces ◽  
Diffraction Data ◽  
X Ray Diffraction ◽  
X Ray ◽  
Custom Made ◽  
Sample Position ◽  
Diffraction Imaging ◽  
Diffraction Patterns ◽  
User Friendly ◽  
Ccd Detectors

Using our custom-made diffraction apparatus KOTOBUKI-1 and two multiport CCD detectors, cryogenic coherent X-ray diffraction imaging experiments have been undertaken at the SPring-8 Angstrom Compact free electron LAser (SACLA) facility. To efficiently perform experiments and data processing, two software suites with user-friendly graphical user interfaces have been developed. The first is a program suite namedIDATEN, which was developed to easily conduct four procedures during experiments: aligning KOTOBUKI-1, loading a flash-cooled sample into the cryogenic goniometer stage inside the vacuum chamber of KOTOBUKI-1, adjusting the sample position with respect to the X-ray beam using a pair of telescopes, and collecting diffraction data by raster scanning the sample with X-ray pulses. NamedG-SITENNO, the other suite is an automated version of the originalSITENNOsuite, which was designed for processing diffraction data. These user-friendly software suites are now indispensable for collecting a large number of diffraction patterns and for processing the diffraction patterns immediately after collecting data within a limited beam time.

The X-Ray Powder Diffraction Patterns and Crystal Structure for Al2M3Y(M=Cu, Ni)

2014 ◽  
Vol 950 ◽  
pp. 48-52
Author(s):  
De Gui Li ◽  
Ming Qin ◽  
Liu Qing Liang ◽  
Zhao Lu ◽  
Shu Hui Liu ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Powder Diffraction ◽  
Diffraction Data ◽  
Hexagonal Structure ◽  
Argon Atmosphere ◽  
X Ray Diffraction ◽  
High Quality ◽  
X Ray ◽  
Arc Melting ◽  
Diffraction Patterns

The Al2M3Y(M=Cu, Ni) compound was synthesized by arc melting under argon atmosphere. The high-quality powder X-ray diffraction data of Al2M3Y have been presented. The refinement of the X-ray diffraction patterns for the Al2M3Y compound show that the Al2M3Y has hexagonal structure, space groupP6/mmm(No.191), with a = b = 5.1618(2) Å, c = 4.1434(1) Å,V= 95.6 Å3,Z= 1,ڑx= 5.7922 g/cm3,F30= 155.5(0.0057, 34), RIR = 2.31 for Al2Cu3Y, and with a = b = 5.0399(1) Å, c = 4.0726(1) Å,V= 89.59 Å3,Z= 1,ڑx= 5.9118 g/cm3,F30= 135.7(0.0072, 30), RIR = 2.54 for Al2Ni3Y.

scholarly journals A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jin-Woong Lee ◽  
Woon Bae Park ◽  
Jin Hee Lee ◽  
Satendra Pal Singh ◽  
Kee-Sun Sohn
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Inorganic Compounds ◽  
Phase Identification ◽  
X Ray Diffraction ◽  
X Ray ◽  
Learning Techniques ◽  
Learning Technique ◽  
Xrd Patterns

AbstractHere we report a facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds. We simulate plausible powder X-ray diffraction (XRD) patterns for 170 inorganic compounds in the Sr-Li-Al-O quaternary compositional pool, wherein promising LED phosphors have been recently discovered. Finally, 1,785,405 synthetic XRD patterns are prepared by combinatorically mixing the simulated powder XRD patterns of 170 inorganic compounds. Convolutional neural network (CNN) models are built and eventually trained using this large prepared dataset. The fully trained CNN model promptly and accurately identifies the constituent phases in complex multiphase inorganic compounds. Although the CNN is trained using the simulated XRD data, a test with real experimental XRD data returns an accuracy of nearly 100% for phase identification and 86% for three-step-phase-fraction quantification.

scholarly journals A convolutional neural network for defect classification in Bragg coherent X-ray diffraction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Bruce Lim ◽  
Ewen Bellec ◽  
Maxime Dupraz ◽  
Steven Leake ◽  
Andrea Resta ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Diffraction Pattern ◽  
Three Dimensional ◽  
X Ray Diffraction ◽  
Coherent Diffraction Imaging ◽  
The Neural Network ◽  
Diffraction Imaging ◽  
Diffraction Patterns ◽  
Defect Recognition

AbstractCoherent diffraction imaging enables the imaging of individual defects, such as dislocations or stacking faults, in materials. These defects and their surrounding elastic strain fields have a critical influence on the macroscopic properties and functionality of materials. However, their identification in Bragg coherent diffraction imaging remains a challenge and requires significant data mining. The ability to identify defects from the diffraction pattern alone would be a significant advantage when targeting specific defect types and accelerates experiment design and execution. Here, we exploit a computational tool based on a three-dimensional (3D) parametric atomistic model and a convolutional neural network to predict dislocations in a crystal from its 3D coherent diffraction pattern. Simulated diffraction patterns from several thousands of relaxed atomistic configurations of nanocrystals are used to train the neural network and to predict the presence or absence of dislocations as well as their type (screw or edge). Our study paves the way for defect-recognition in 3D coherent diffraction patterns for material science.

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