دورية أكاديمية
Application of self-supervised approaches to the classification of X-ray diffraction spectra during phase transitions
| العنوان: | Application of self-supervised approaches to the classification of X-ray diffraction spectra during phase transitions |
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| المؤلفون: | Yue Sun, Sandor Brockhauser, Péter Hegedűs, Christian Plückthun, Luca Gelisio, Danilo Enoque Ferreira de Lima |
| المصدر: | Scientific Reports, Vol 13, Iss 1, Pp 1-16 (2023) |
| بيانات النشر: | Nature Portfolio, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | Abstract Spectroscopy and X-ray diffraction techniques encode ample information on investigated samples. The ability of rapidly and accurately extracting these enhances the means to steer the experiment, as well as the understanding of the underlying processes governing the experiment. It improves the efficiency of the experiment, and maximizes the scientific outcome. To address this, we introduce and validate three frameworks based on self-supervised learning which are capable of classifying 1D spectral curves using data transformations preserving the scientific content and only a small amount of data labeled by domain experts. In particular, in this work we focus on the identification of phase transitions in samples investigated by x-ray powder diffraction. We demonstrate that the three frameworks, based either on relational reasoning, contrastive learning, or a combination of the two, are capable of accurately identifying phase transitions. Furthermore, we discuss in detail the selection of data augmentation techniques, crucial to ensure that scientifically meaningful information is retained. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| العلاقة: | https://doaj.org/toc/2045-2322Test |
| DOI: | 10.1038/s41598-023-36456-y |
| الوصول الحر: | https://doaj.org/article/e3c12ff033c04572a97c09f7d4e45ee9Test |
| رقم الانضمام: | edsdoj.3c12ff033c04572a97c09f7d4e45ee9 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-023-36456-y |