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@ARTICLE{Sun:598141,
      author       = {Sun, Yue and Brockhauser, Sandor and Hegedűs, Péter and
                      Plückthun, Christian and Gelisio, Luca and Ferreira de
                      Lima, Danilo Enoque},
      title        = {{A}pplication of self-supervised approaches to the
                      classification of {X}-ray diffraction spectra during phase
                      transitions},
      journal      = {Scientific reports},
      volume       = {13},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {PUBDB-2023-06752},
      pages        = {9370},
      year         = {2023},
      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.},
      cin          = {DOOR ; HAS-User / FS-PETRA-S / $XFEL_DO_DD_DA$},
      ddc          = {600},
      cid          = {I:(DE-H253)HAS-User-20120731 /
                      I:(DE-H253)FS-PETRA-S-20210408 /
                      $I:(DE-H253)XFEL_DO_DD_DA-20210408$},
      pnm          = {631 - Matter – Dynamics, Mechanisms and Control
                      (POF4-631) / 6G3 - PETRA III (DESY) (POF4-6G3) / DFG project
                      G:(GEPRIS)460197019 - FAIRmat – FAIRe Dateninfrastruktur
                      für die Physik der kondensierten Materie und die chemische
                      Physik fester Stoffe (460197019)},
      pid          = {G:(DE-HGF)POF4-631 / G:(DE-HGF)POF4-6G3 /
                      G:(GEPRIS)460197019},
      experiment   = {EXP:(DE-H253)P-P02.2-20150101},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37296300},
      UT           = {WOS:001006690200063},
      doi          = {10.1038/s41598-023-36456-y},
      url          = {https://bib-pubdb1.desy.de/record/598141},
}