Machine-learning-based prediction of first-principles XANES spectra for amorphous materials

Hirai, Haruki; Iizawa, Takumi; Tamura, Tomoyuki; Karasuyama, Masayuki; Kobayashi, Ryo; Hirose, Takakazu

doi:10.1103/physrevmaterials.6.115601

Cited by 3 publications

(2 citation statements)

References 51 publications

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“…The authors ascribe this improvement to the explicit inclusion of bond lengths and angles which influence XANES spectra. Hirai et al [107] used linear regression of the input descriptors (LMBRT, SOAP and ACSF) to predict and interpret the XANES spectra of amorphous Si and SiO 2 with SOAP displaying the lowest mean squared error.…”

Section: Mbtrmentioning

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: Mbtrmentioning

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.

View full text Add to dashboard Cite

show abstract

“…Rankine & Penfold (2022) utilized theoretical metal K-edge spectra for more than 200 000 structures in The Materials Project (Jain et al, 2013) and trained the deep neural network which predicts the XANES spectrum for any given structure. A comprehensive prediction of Si K-edge XANES spectra was performed based on an atom-centered symmetry function, smooth overlap of atomic positions, local many-body tensor representation, and spectral neighbor analysis potential (Hirai et al, 2022). Successfully refining the quality of prediction and eliminating systematic differences between theory and experiment, such approaches may replace calculation software in the future.…”

Section: Introductionmentioning

confidence: 99%

Improving sensitivity of XANES structural fit to the bridged metal–metal coordination

Abrosimov,

Protsenko,

Mannaa

et al. 2024

J Synchrotron Radiat

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Hard X-ray absorption spectroscopy is a valuable in situ probe for non-destructive diagnostics of metal sites. The low-energy interval of a spectrum (XANES) contains information about the metal oxidation state, ligand type, symmetry and distances in the first coordination shell but shows almost no dependency on the bridged metal–metal bond length. The higher-energy interval (EXAFS), on the contrary, is more sensitive to the coordination numbers and can decouple the contribution from distances in different coordination shells. Supervised machine-learning methods can combine information from different intervals of a spectrum; however, computational approaches for the near-edge region of the spectrum and higher energies are different. This work aims to keep all benefits of XANES and extend its sensitivity towards the interatomic distances in the first and second coordination shells. Using a binuclear bridged copper complex as a case study and cross-validation analysis as a quantitative tool it is shown that the first 170 eV above the edge are already sufficient to balance the contributions of Cu–O/N scattering and Cu–Cu scattering. As a more general outcome this work highlights the trivial but often overlooked importance of using `longer' energy intervals of XANES for structural refinement and machine-learning predictions. The first 200 eV above the absorption edge still do not require parametrization of Debye–Waller damping and can be calculated within full multiple scattering or finite difference approximations with only moderately increased computational costs.

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Updates to the DScribe library: New descriptors and derivatives

Laakso

Himanen

Homm

et al. 2023

The Journal of Chemical Physics

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We present an update of the DScribe package, a Python library for atomistic descriptors. The update extends DScribe’s descriptor selection with the Valle–Oganov materials fingerprint and provides descriptor derivatives to enable more advanced machine learning tasks, such as force prediction and structure optimization. For all descriptors, numeric derivatives are now available in DScribe. For the many-body tensor representation (MBTR) and the Smooth Overlap of Atomic Positions (SOAP), we have also implemented analytic derivatives. We demonstrate the effectiveness of the descriptor derivatives for machine learning models of Cu clusters and perovskite alloys.

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Machine-learning-based prediction of first-principles XANES spectra for amorphous materials

Cited by 3 publications

References 51 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

Improving sensitivity of XANES structural fit to the bridged metal–metal coordination

Updates to the DScribe library: New descriptors and derivatives

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