Adaptive design of an X-ray magnetic circular dichroism spectroscopy experiment with Gaussian process modelling
| Autor: | Masahiro Sawada, Kanta Ono, Ai Hashimoto, Yasuo Takeichi, Hideitsu Hino, Tetsuro Ueno |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Adaptive sampling
Materials science 02 engineering and technology 01 natural sciences symbols.namesake 0103 physical sciences lcsh:TA401-492 General Materials Science 010306 general physics Spectroscopy Gaussian process lcsh:Computer software Magnetic moment Magnetic circular dichroism 021001 nanoscience & nanotechnology Computer Science Applications Computational physics Wavelength Data point lcsh:QA76.75-76.765 X-ray magnetic circular dichroism Mechanics of Materials Modeling and Simulation symbols lcsh:Materials of engineering and construction. Mechanics of materials 0210 nano-technology |
| Zdroj: | npj Computational Materials, Vol 4, Iss 1, Pp 1-8 (2018) |
| ISSN: | 2057-3960 |
| DOI: | 10.1038/s41524-017-0057-4 |
| Popis: | Spectroscopy is a widely used experimental technique, and enhancing its efficiency can have a strong impact on materials research. We propose an adaptive design for spectroscopy experiments that uses a machine learning technique to improve efficiency. We examined X-ray magnetic circular dichroism (XMCD) spectroscopy for the applicability of a machine learning technique to spectroscopy. An XMCD spectrum was predicted by Gaussian process modelling with learning of an experimental spectrum using a limited number of observed data points. Adaptive sampling of data points with maximum variance of the predicted spectrum successfully reduced the total data points for the evaluation of magnetic moments while providing the required accuracy. The present method reduces the time and cost for XMCD spectroscopy and has potential applicability to various spectroscopies. Machine learning methods can make spectroscopy more time- and cost-efficient. Spectroscopy is a powerful experimental technique for characterising the properties of materials, but measurement times can often be long resulting in high running costs. There are many different types of spectroscopy, using light at different wavelengths. A team of Japanese researchers led by Tetsuro Ueno and Kanta Ono now show that machine learning methods can be used to reduce the number of data points required to determine the magnetic moments in a material using x-ray magnetic circular dichroism spectroscopy. This method, which repeatedly adapts the experimental sampling based on model predictions, not only reduces the time and cost for this type of spectroscopy, but it should be applicable to others. |
| Databáze: | OpenAIRE |
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