Fingerprinting shock-induced deformations via diffraction

Mishra, Avanish; Kunka, Cody; Echeverria, Marco; Dingreville, Remi Philippe Michel; Dongare, Avinash M.

doi:10.1038/s41598-021-88908-y

Cited by 11 publications

(2 citation statements)

References 69 publications

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“…As pointed out by Weidenthaler, in many cases the evaluation and interpretation of the results from these 1D spectroscopic profiles can be difficult or erroneous, especially when the material being probed deviates from a pristine, defect-free state to being in a deformed and defected state. Even though spectroscopic measurements are often accompanied by density functional theory (DFT) or molecular dynamics (MD) simulations to facilitate their interpretation, − regressing changes in the vibrational properties to the state of the material remains a challenging problem, notably when multiple microstructural sources simultaneously affect changes in the vibrational spectrum. These challenges are associated with identifying key representative features in the spectral profile and relating their characteristics to the structural characterization.…”

Section: Introductionmentioning

confidence: 99%

Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis

et al. 2023

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Vibrational spectroscopy is a nondestructive technique commonly used in chemical and physical analyses to determine atomic structures and associated properties. However, the evaluation and interpretation of spectroscopic profiles based on human-identifiable peaks can be difficult and convoluted. To address this challenge, we present a reliable protocol based on supervised manifold learning techniques meant to connect vibrational spectra to a variety of complex and diverse atomic structure configurations. As an illustration, we examined a large database of virtual vibrational spectroscopy profiles generated from atomistic simulations for silicon structures subjected to different stress, amorphization, and disordering states. We evaluated representative features in those spectra via various linear and nonlinear dimensionality reduction techniques and used the reduced representation of those features with decision trees to correlate them with structural information unavailable through classical human-identifiable peak analysis. We show that our trained model accurately (over 97% accuracy) and robustly (insensitive to noise) disentangles the contribution from the different material states, hence demonstrating a comprehensive decoding of spectroscopic profiles beyond classical (human-identifiable) peak analysis.

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Section: Introductionmentioning

confidence: 99%

Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis

et al. 2023

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“…Fingerprint plays an important role in identity security worldwide [1][2][3]. The fingerprint features can be divided into three levels [4].…”

Section: Introductionmentioning

confidence: 99%

Subcutaneous sweat pore estimation from optical coherence tomography

Ding

Wang

Chen

et al. 2021

IET Image Processing

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Sweat pore, one of the level 3 features of fingerprint, has attracted much attention in fingerprint recognition. Traditional sweat pores on surface fingerprint are unclear or blurred when fingers are stained or damaged. Subcutaneous sweat pores, as cross section of the sweat glands, are resistant to external interferences. With 3D fingertip information measured by optical coherence tomography (OCT), the subcutaneous sweat pore estimation from OCT volume data is investigated. First, an adaptive subcutaneous pore image reconstruction method is proposed. It utilizes the skin surface and viable epidermis junction as reference and realizes depth-adaptive pore image reconstruction. Second, a dilated U-Net combining the U-Net with dilated convolution is proposed for subcutaneous sweat pore extraction, which can prevent information loss of sweat pores caused by downsampling. To the best knowledge, it is the first time that subcutaneous sweat pore extraction is investigated and proposed. Experiments on subcutaneous pore image reconstruction and sweat pore extraction are both conducted. The qualitative and quantitative results show that the proposed adaptive method performs better in subcutaneous pore image reconstruction compared with the fix-depth method, and the dilated U-Net outperforms other methods on subcutaneous sweat pore extraction.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Virtual diffraction simulations using the quasi-coarse-grained dynamics method to understand and interpret plasticity contributions during in situ shock experiments

Mishra

Dongare

2022

J Mater Sci

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Fingerprinting shock-induced deformations via diffraction

Cited by 11 publications

References 69 publications

Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis

Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis

Subcutaneous sweat pore estimation from optical coherence tomography

Virtual diffraction simulations using the quasi-coarse-grained dynamics method to understand and interpret plasticity contributions during in situ shock experiments

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