Automatic determination of the spectrum–structure relationship by tree structure-based unsupervised and supervised learning

Kiyohara, Shin; Kikumasa, Kakeru; Shibata, Kiyou; Mizoguchi, Teruyasu

doi:10.1016/j.ultramic.2021.113438

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…CC BY 4.0. combination clustering and decision-tree ML model where, for a selection of oxygen and carbon K-edge XAS spectra, categories of spectra were first clustered together and a decision-tree model was used to derive subsequently the correspondence between the distinctive x-ray spectral features characterising each cluster of x-ray spectra and the geometric properties. of interest [187]. In addition to these classification ML models, an earlier study by Kiyohara and Mizoguchi [188] and a study from Higashi and Ikeno [189] both reported regression ML models for mapping x-ray spectra onto two-body PDFs and applied these to the analysis of oxygen K-edge XAS spectra.…”

Section: Reverse Mapping: Structure ← Spectrummentioning

confidence: 99%

Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy

Penfold,

Watson,

Middleton

et al. 2024

Mach. Learn.: Sci. Technol.

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Computational spectroscopy has emerged as a critical tool for researchers looking to achieve both qualitative and quantitative interpretations of experimental spectra. Over the past decade, increased interactions between experiment and theory have created a positive feedback loop that has stimulated developments in both domains. In particular, the increased accuracy of calculations has led to them becoming an indispensable tool for the analysis of spectroscopies across the electromagnetic spectrum. This progress is especially well demonstrated for short-wavelength techniques, e.g. core-hole (X-ray) spectroscopies, whose prevalence has increased following the advent of modern X-ray facilities including third-generation synchrotrons and X-ray free-electron lasers (XFELs). While calculations based on well-established wavefunction or density-functional methods continue to dominate the greater part of spectral analyses in the literature, emerging developments in machine-learning algorithms are beginning to open up new opportunities to complement these traditional techniques with fast, accurate, and affordable 'black-box' approaches. This Topical Review recounts recent progress in data-driven/machine-learning approaches for computational X-ray spectroscopy. We discuss the achievements and limitations of the presently-available approaches and review the potential that these techniques have to expand the scope and reach of computational and experimental X-ray spectroscopic studies.

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Section: Reverse Mapping: Structure ← Spectrummentioning

confidence: 99%

Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy

Penfold,

Watson,

Middleton

et al. 2024

Mach. Learn.: Sci. Technol.

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Ceramic science of crystal defect cores

Matsunaga

Yoshiya

Shibata

et al. 2022

J. Ceram. Soc. Japan

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Point defects, dislocations, grain boundaries and interfaces are always involved in ceramic microstructures and play important roles for physical and chemical properties of ceramics. Currently, proper control of these crystal defects is inevitable to tailor ceramic materials with superior properties. This article reviews recent research projects on distinct properties and phenomena in ceramics due to crystal defects. In particular, we would like to emphasize importance of central core regions of crystal defects, namely, "crystal defect cores". They have specific electronic and atomic structures that are different from those in bulk. Recent advances of nanoscale characterizations and theoretical calculations make it possible to acquire a variety of quantitative data on electronic structures enclosed at the crystal-defect cores, which gives clear understanding of various ceramic properties at the electronic and atomic levels.

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Automatic determination of the spectrum–structure relationship by tree structure-based unsupervised and supervised learning

Cited by 2 publications

References 36 publications

Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy

Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy

Ceramic science of crystal defect cores

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