Adaptive design of an X-ray magnetic circular dichroism spectroscopy experiment with Gaussian process modelling

Ueno, Tetsuro; Hino, Hideitsu; Hashimoto, Ai; Takeichi, Yasuo; Sawada, Masahiro; Ono, Kanta

doi:10.1038/s41524-017-0057-4

Cited by 43 publications

(36 citation statements)

References 31 publications

(31 reference statements)

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“…For example, experimental parameters such as scanning ranges, data point intervals, and measurement time can be objectively tailored for a specific purpose if an ML model indicates guidelines for these parameters to assure minimal but sufficient data quality for the purpose. An efficient experimental design is particularly crucial for experiments using synchrotron X-rays and neutron beams, where the efficient use of the measurement time is essential because of limited beamtime available 52 – 54 .…”

Section: Discussionmentioning

confidence: 99%

Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach

Suzuki

Hino

Hawai

et al. 2020

Sci Rep

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Determination of crystal system and space group in the initial stages of crystal structure analysis forms a bottleneck in material science workflow that often requires manual tuning. Herein we propose a machine-learning (ML)-based approach for crystal system and space group classification based on powder X-ray diffraction (XRD) patterns as a proof of concept using simulated patterns. Our tree-ensemble-based ML model works with nearly or over 90% accuracy for crystal system classification, except for triclinic cases, and with 88% accuracy for space group classification with five candidates. We also succeeded in quantifying empirical knowledge vaguely shared among experts, showing the possibility for data-driven discovery of unrecognised characteristics embedded in experimental data by using an interpretable ML approach.

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Section: Discussionmentioning

confidence: 99%

Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach

Suzuki

Hino

Hawai

et al. 2020

Sci Rep

Self Cite

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“…Using the proposed method, a machine learning model could be constructed according to a specific experimental setup (i.e., depending on the 2# range or X-ray wavelength). Thus, we streamlined the materials discovery workflow, and the accelerated characterization of materials will be realized by combining high-throughput experiments through optimal measurements [6] and on-the-fly data analysis (as shown in this research) and knowledge acquisition from the data [7].…”

Section: Resultsmentioning

confidence: 99%

Machine Learning-based Crystal Structure Prediction for X-Ray Microdiffraction

et al. 2018

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“…The objective of efficient materials research with materials informatics is to eliminate the bottleneck, and to accelerate the research flow consisting of the fabrication and characterisation of materials followed by data analysis. [5][6][7] It is thus important to establish a methodology that automatically and quantitatively extracts the materials parameter from the measured data. 8,9 This technique allows the on-the-fly data analysis to be completed as part of the online characterisation in that it provides a combined procedure ranging from material fabrication to material discovery, thereby eliminating the bottleneck.…”

Section: Introductionmentioning

confidence: 99%

Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures

et al. 2019

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Materials informatics has significantly accelerated the discovery and analysis of materials in the past decade. One of the key contributors to accelerated materials discovery is the use of on-the-fly data analysis with high-throughput experiments, which has given rise to the need for accelerated and accurate automated estimation of the properties of materials. In this regard, spectroscopic data are widely used for materials discovery because these data include essential information about materials. An important requirement for the realisation of the automated estimation of materials parameters is the selection of a similarity measure, or kernel function. The required measure should be robust in terms of peak shifting, peak broadening, and noise. However, the determination of appropriate similarity measures for spectra and the automated estimation of materials parameters from these spectra currently remain unresolved. We examined major similarity measures to evaluate the similarity of both X-ray absorption and electron energy-loss spectra. The similarity measures show good correspondence with the materials parameter, that is, the crystal-field parameter, in all measures. The Pearson's correlation coefficient was the highest for the robustness against noise and peak broadening. We obtained the regression model for the crystal-field parameter 10 Dq from the similarity of the spectra. The regression model enabled the materials parameter, that is, 10 Dq, to be automatically estimated from the spectra. With regard to research progress in similarity measures, this methodology would make it possible to extract the materials parameter from a large-scale dataset of experimental data.

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Adaptive design of an X-ray magnetic circular dichroism spectroscopy experiment with Gaussian process modelling

Cited by 43 publications

References 31 publications

Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach

Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach

Machine Learning-based Crystal Structure Prediction for X-Ray Microdiffraction

Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures

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