A data-driven XRD analysis protocol for phase identification and phase-fraction prediction of multiphase inorganic compounds

2021 ◽  
Author(s):  
Jin-Woong Lee ◽  
Woon Bae Park ◽  
Minseuk Kim ◽  
Satendra Pal Singh ◽  
Myoungho Pyo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Inorganic Compounds ◽  
Xrd Analysis ◽  
Data Driven ◽  
Phase Fraction ◽  
Phase Identification ◽  
Constituent Phase ◽  
Analysis Protocol

Deep learning (DL) models trained with synthetic XRD data have never accomplished a satisfactory quantitative XRD analysis for the exact prediction of a constituent-phase fraction in unknown multiphase inorganic compounds,...

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 High-Resolution ISAR Imaging and Autofocusing via 2D-ADMM-Net

2021 ◽  
Vol 13 (12) ◽  
pp. 2326
Author(s):  
Xiaoyong Li ◽  
Xueru Bai ◽  
Feng Zhou
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Phase Error ◽  
Random Phase ◽  
Back Propagation ◽  
Estimation Method ◽  
Data Driven ◽  
Reconstruction Performance ◽  
Imaging Results ◽  
Isar Imaging

A deep-learning architecture, dubbed as the 2D-ADMM-Net (2D-ADN), is proposed in this article. It provides effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging under scenarios of low SNRs and incomplete data, by combining model-based sparse reconstruction and data-driven deep learning. Firstly, mapping from ISAR images to their corresponding echoes in the wavenumber domain is derived. Then, a 2D alternating direction method of multipliers (ADMM) is unrolled and generalized to a deep network, where all adjustable parameters in the reconstruction layers, nonlinear transform layers, and multiplier update layers are learned by an end-to-end training through back-propagation. Since the optimal parameters of each layer are learned separately, 2D-ADN exhibits more representation flexibility and preferable reconstruction performance than model-driven methods. Simultaneously, it is able to better facilitate ISAR imaging with limited training samples than data-driven methods owing to its simple structure and small number of adjustable parameters. Additionally, benefiting from the good performance of 2D-ADN, a random phase error estimation method is proposed, through which well-focused imaging can be acquired. It is demonstrated by experiments that although trained by only a few simulated images, the 2D-ADN shows good adaptability to measured data and favorable imaging results with a clear background can be obtained in a short time.

scholarly journals An Insight into Chemistry and Structure of Colloidal 2D-WS2 Nanoflakes: Combined XPS and XRD Study

Nanomaterials ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1969
Author(s):  
Riccardo Scarfiello ◽  
Elisabetta Mazzotta ◽  
Davide Altamura ◽  
Concetta Nobile ◽  
Rosanna Mastria ◽  
...  
Keyword(s):  
Photoelectron Spectroscopy ◽  
Xrd Analysis ◽  
Crystal Habit ◽  
Phase Identification ◽  
X Ray Diffraction ◽  
X Ray ◽  
Morphological Evaluation ◽  
Rational Understanding ◽  
Structural Inspection

The surface and structural characterization techniques of three atom-thick bi-dimensional 2D-WS2 colloidal nanocrystals cross the limit of bulk investigation, offering the possibility of simultaneous phase identification, structural-to-morphological evaluation, and surface chemical description. In the present study, we report a rational understanding based on X-ray photoelectron spectroscopy (XPS) and structural inspection of two kinds of dimensionally controllable 2D-WS2 colloidal nanoflakes (NFLs) generated with a surfactant assisted non-hydrolytic route. The qualitative and quantitative determination of 1T’ and 2H phases based on W 4f XPS signal components, together with the presence of two kinds of sulfur ions, S22− and S2−, based on S 2p signal and related to the formation of WS2 and WOxSy in a mixed oxygen-sulfur environment, are carefully reported and discussed for both nanocrystals breeds. The XPS results are used as an input for detailed X-ray Diffraction (XRD) analysis allowing for a clear discrimination of NFLs crystal habit, and an estimation of the exact number of atomic monolayers composing the 2D-WS2 nanocrystalline samples.

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

scholarly journals Data-Driven Intelligent Efficient Synaptic Storage for Deep Learning

2017 ◽  
Vol 64 (12) ◽  
pp. 1412-1416 ◽  
Author(s):  
Jonathon Edstrom ◽  
Yifu Gong ◽  
Dongliang Chen ◽  
Jinhui Wang ◽  
Na Gong
Keyword(s):  
Deep Learning ◽  
Data Driven
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