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

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.

Infrared spectroscopic study of the YPO4-YCrO4 system

1985 ◽  
Vol 50 (10) ◽  
pp. 2139-2145
Author(s):  
Alexander Muck ◽  
Eva Šantavá ◽  
Bohumil Hájek
Keyword(s):  
Spectroscopic Study ◽  
Infrared Spectra ◽  
Point Of View ◽  
Mixed Crystals ◽  
Crystal Symmetry ◽  
Infrared Spectroscopic Study ◽  
X Ray Diffraction ◽  
X Ray ◽  
Infrared Spectroscopic ◽  
Diffraction Patterns

The infrared spectra and powder X-ray diffraction patterns of polycrystalline YPO4-YCrO4 samples are studied from the point of view of their crystal symmetry. Mixed crystals of the D4h19 symmetry are formed over the region of 0-30 mol.% YPO4 in YCrO4. The Td → D2d → D2 or C2v(GS eff) correlation is appropriate for both PO43- and CrO43- anions.

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.

THE XRD STUDY OF THE EFFECT OF SLIGHT CHANGE IN STRUCTURE ON THE SUPERCONDUCTIVITY OF Y-Ba-Cu-O SYSTEM MATERIAL

1987 ◽  
Vol 01 (07n08) ◽  
pp. 289-293 ◽  
Author(s):  
WANG HUAQIN ◽  
ZHANG SHIYUAN ◽  
JIN TONGZHENG ◽  
HAN SHIYING ◽  
QIU DIRONG ◽  
...  
Keyword(s):  
Single Phase ◽  
Crystal Symmetry ◽  
Epr Spectra ◽  
Processing Conditions ◽  
X Ray Diffraction ◽  
X Ray ◽  
Xrd Study ◽  
The Difference ◽  
Diffraction Patterns ◽  
Flowing Oxygen

In this paper the differences in diffraction intensities from some crystal planes in the X-ray diffraction patterns of high Tc Y-Ba-Cu-O system superconductors prepared by different processing conditions and the difference among various structure cells in references are interpreted using computer fitting. The results suggest that there exists two structure cells in the single phase YBa2Cu3O7−x samples. Both structure cells have the same crystal symmetry and almost the same lattice parameters, a=3.821Å, b=3.892Å and c=11.676Å, but the different distortion degree of Cu2-O plane. According to EPR spectra measured on the same samples, it is considered that the improvement of superconductivity for the samples prepared by two-step annealing in flowing oxygen may be related to concentration of the structure cell with more serious distortion on the Cu2-O plane.

scholarly journals Neural network based classification of crystal symmetries from x-ray diffraction patterns

2019 ◽  
Vol 99 (24) ◽  
Author(s):  
Pascal Marc Vecsei ◽  
Kenny Choo ◽  
Johan Chang ◽  
Titus Neupert
Keyword(s):  
Neural Network ◽  
X Ray Diffraction ◽  
X Ray ◽  
Diffraction Patterns

Evaluation of Freezing Methods by Low Temperature X-Ray Diffraction

1977 ◽  
Vol 35 ◽  
pp. 330-333
Author(s):  
T. Gulik-Krzywicki ◽  
M.J. Costello
Keyword(s):  
Biological Materials ◽  
Molecular Organization ◽  
X Ray Diffraction ◽  
Glass Capillary ◽  
X Ray ◽  
Rate Dependent ◽  
Before And After ◽  
Freeze Etching ◽  
Diffraction Patterns ◽  
Set Up

Freeze-etching electron microscopy is currently one of the best methods for studying molecular organization of biological materials. Its application, however, is still limited by our imprecise knowledge about the perturbations of the original organization which may occur during quenching and fracturing of the samples and during the replication of fractured surfaces. Although it is well known that the preservation of the molecular organization of biological materials is critically dependent on the rate of freezing of the samples, little information is presently available concerning the nature and the extent of freezing-rate dependent perturbations of the original organizations. In order to obtain this information, we have developed a method based on the comparison of x-ray diffraction patterns of samples before and after freezing, prior to fracturing and replication.Our experimental set-up is shown in Fig. 1. The sample to be quenched is placed on its holder which is then mounted on a small metal holder (O) fixed on a glass capillary (p), whose position is controlled by a micromanipulator.

Examination of Rabbit Fc Crystals

1968 ◽  
Vol 26 ◽  
pp. 140-141
Author(s):  
J. P. Robinson ◽  
P. G. Lenhert
Keyword(s):  
Molecular Weight ◽  
Flat Plate ◽  
Phosphate Buffer ◽  
Asymmetric Unit ◽  
X Ray Diffraction ◽  
X Ray ◽  
Neutral Ph ◽  
Small Crystals ◽  
Carbon Coated ◽  
Diffraction Patterns

Crystallographic studies of rabbit Fc using X-ray diffraction patterns were recently reported. The unit cell constants were reported to be a = 69. 2 A°, b = 73. 1 A°, c = 60. 6 A°, B = 104° 30', space group P21, monoclinic, volume of asymmetric unit V = 148, 000 A°3. The molecular weight of the fragment was determined to be 55, 000 ± 2000 which is in agreement with earlier determinations by other methods.Fc crystals were formed in water or dilute phosphate buffer at neutral pH. The resulting crystal was a flat plate as previously described. Preparations of small crystals were negatively stained by mixing the suspension with equal volumes of 2% silicotungstate at neutral pH. A drop of the mixture was placed on a carbon coated grid and allowed to stand for a few minutes. The excess liquid was removed and the grid was immediately put in the microscope.

Microstructure and crystal symmetry of Pb2Sr2(Y/Ca)Cu3O9−δ

1990 ◽  
Vol 48 (4) ◽  
pp. 64-65
Author(s):  
Y. P. Lin ◽  
J. S. Xue ◽  
J. E. Greedan
Keyword(s):  
Electron Diffraction ◽  
High Temperature Superconductors ◽  
Carbon Film ◽  
Basic Structure ◽  
Crystal Symmetry ◽  
X Ray Diffraction ◽  
X Ray ◽  
Space Groups ◽  
New Family ◽  
Convergent Beam

A new family of high temperature superconductors based on Pb2Sr2YCu3O9−δ has recently been reported. One method of improving Tc has been to replace Y partially with Ca. Although the basic structure of this type of superconductors is known, the detailed structure is still unclear, and various space groups has been proposed. In our work, crystals of Pb2Sr2YCu3O9−δ with dimensions up to 1 × 1 × 0.25.mm and with Tc of 84 K have been grown and their superconducting properties described. The defects and crystal symmetry have been investigated using electron microscopy performed on crushed crystals supported on a holey carbon film.Electron diffraction confirmed x-ray diffraction results which showed that the crystals are primitive orthorhombic with a=0.5383, b=0.5423 and c=1.5765 nm. Convergent Beam Electron Diffraction (CBED) patterns for the and axes are shown in Figs. 1 and 2 respectively.

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