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000598141 1001_ $$0P:(DE-H253)PIP1091503$$aSun, Yue$$b0$$eCorresponding author$$udesy
000598141 245__ $$aApplication of self-supervised approaches to the classification of X-ray diffraction spectra during phase transitions
000598141 260__ $$a[London]$$bMacmillan Publishers Limited, part of Springer Nature$$c2023
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000598141 520__ $$aSpectroscopy 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.
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000598141 7001_ $$0P:(DE-H253)PIP1029958$$aBrockhauser, Sandor$$b1
000598141 7001_ $$0P:(DE-HGF)0$$aHegedűs, Péter$$b2$$eCorresponding author
000598141 7001_ $$0P:(DE-H253)PIP1032242$$aPlückthun, Christian$$b3
000598141 7001_ $$0P:(DE-H253)PIP1028523$$aGelisio, Luca$$b4
000598141 7001_ $$0P:(DE-H253)PIP1028636$$aFerreira de Lima, Danilo Enoque$$b5$$eCorresponding author
000598141 773__ $$0PERI:(DE-600)2615211-3$$a10.1038/s41598-023-36456-y$$gVol. 13, no. 1, p. 9370$$n1$$p9370$$tScientific reports$$v13$$x2045-2322$$y2023
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