Ultrafast X-Ray Diffraction Visualization of 
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 Phase Transition in KCl under Shock Compression

Zhang, Y. Y.; Li, Y.X.; Fan, D.; Zhang, N. B.; Huang, Jiawei; Tang, Mingjie; Cai, Y.; Zeng, X. L.; Sun, Tao; Fezzaa, Kamel; Chen, S.; Luo, Sheng‐Nian

doi:10.1103/physrevlett.127.045702

Cited by 12 publications

(6 citation statements)

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“…The good agreement between experiments and simulations provides a full atomic-level picture of the ultrafast elastic–plastic deformation in single-crystal materials. The techniques demonstrated here will open up a new horizon for investigating a broad range of high-pressure and high strain-rate phenomena 23 – 25 , including dynamic plastic deformation 26 and phase transitions 27 , 28 .…”

Section: Resultsmentioning

confidence: 99%

Ultrafast visualization of incipient plasticity in dynamically compressed matter

Tang

Chen

et al. 2022

Nat Commun

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Plasticity is ubiquitous and plays a critical role in material deformation and damage; it inherently involves the atomistic length scale and picosecond time scale. A fundamental understanding of the elastic-plastic deformation transition, in particular, incipient plasticity, has been a grand challenge in high-pressure and high-strain-rate environments, impeded largely by experimental limitations on spatial and temporal resolution. Here, we report femtosecond MeV electron diffraction measurements visualizing the three-dimensional (3D) response of single-crystal aluminum to the ultrafast laser-induced compression. We capture lattice transitioning from a purely elastic to a plastically relaxed state within 5 ps, after reaching an elastic limit of ~25 GPa. Our results allow the direct determination of dislocation nucleation and transport that constitute the underlying defect kinetics of incipient plasticity. Large-scale molecular dynamics simulations show good agreement with the experiment and provide an atomic-level description of the dislocation-mediated plasticity.

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Section: Resultsmentioning

confidence: 99%

Ultrafast visualization of incipient plasticity in dynamically compressed matter

Tang

Chen

et al. 2022

Nat Commun

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“…To exactly obtain the single crystal orientation relative to the incident X-ray, we use forward simulation of the Laue diffraction to index our X-ray diffraction patterns with the Miller indices 33 , 34 . Given the known information, including the experimental geometry and the “white beam” synchrotron X-ray source spectrum, we enumerate all possible orientations and calculate the corresponding X-ray diffraction patterns on the detector, which are compared with a measured diffraction pattern to find the best match 51 , 52 .…”

Section: Methodsmentioning

confidence: 99%

In situ observation of crystal rotation in Ni-based superalloy during additive manufacturing process

Zhang

Liu

et al. 2023

Nat Commun

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Understanding the dynamic process of epitaxial microstructure forming in laser additive manufacturing is very important for achieving products with a single crystalline texture. Here, we perform in situ, real-time synchrotron Laue diffraction experiments to capture the microstructural evolution of nickel-based single-crystal superalloys during the rapid laser remelting process. In situ synchrotron radiation Laue diffraction characterises the crystal rotation behaviour and stray grain formation process. With a complementary thermomechanical coupled finite element simulation and molecular dynamics simulation, we identify that the crystal rotation is governed by the localised heating/cooling heterogeneity-induced deformation gradient and recognise that the sub-grain rotation caused by rapid dislocation movement could be the origin of granular stray grains at the bottom of the melt pool.

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“…We explore the application of selfsupervised relational reasoning and contrastive learning to 1D spectral classi cation problems. In particular, we demonstrate that it can be effectively used to classify phase transitions observed in X-ray diffraction (XRD) experiments [45][46][47] . We introduce and discuss three self-supervised representation learning frameworks for the classi cation of data, namely SpecRR-Net, SpecMoco-Net, and SpecRRMoco-Net.…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Approaches to the Classification of Spectra: Application to Phase Transitions in X-ray Diffraction Data

Sun

Brockhauser

Hegedűs

et al. 2023

Preprint

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The ability to detect interesting events is instrumental to effectively steer experiments and maximize their scientific efficiency. To address this, here we introduce and validate three frameworks based on self-supervised learning which are capable of classifying 1D spectral data using a limited amount of labeled data. In particular, in this work we focus on the identification of phase transitions in samples investigated by x-ray diffraction. We demonstrate that the three frameworks, based either on relational reasoning, contrastive learning, or a combination of the two, are capable of accurately identifying phase transitions. Furthermore, we discuss in detail the selection of data augmentations, crucial to ensure that scientifically meaningful information is retained.

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Ultrafast X-Ray Diffraction Visualization of B1−B2 Phase Transition in KCl under Shock Compression

Cited by 12 publications

References 50 publications

Ultrafast visualization of incipient plasticity in dynamically compressed matter

Ultrafast visualization of incipient plasticity in dynamically compressed matter

In situ observation of crystal rotation in Ni-based superalloy during additive manufacturing process

Self-Supervised Approaches to the Classification of Spectra: Application to Phase Transitions in X-ray Diffraction Data

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