Auflistung Geochemie, Mineralogie, Petrologie nach Schlagwort "machine learning"

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    • Automatic bad‐pixel mask maker for X‐ray pixel detectors with application to serial crystallography 

      Sadri, Alireza; Hadian-Jazi, Marjan; Yefanov, Oleksandr; Galchenkova, Marina; Kirkwood, Henry; Mills, Grant; Sikorski, Marcin; Letrun, Romain; de Wijn, Raphael; Vakili, Mohammad; Oberthuer, Dominik; Komadina, Dana; Brehm, Wolfgang; Mancuso, Adrian P.; Carnis, Jerome; Gelisio, Luca; Chapman, Henry N. (Journal of Applied Crystallography, 2022-11-21)
      X‐ray crystallography has witnessed a massive development over the past decade, driven by large increases in the intensity and brightness of X‐ray sources and enabled by employing high‐frame‐rate X‐ray detectors. The ...

    • Chemical Geodynamics Insights From a Machine Learning Approach 

      Stracke, A.ORCIDiD; Willig, M.; Genske, F.ORCIDiD; Béguelin, P.; Todd, E. (Geochemistry, Geophysics, Geosystems, 2022-10-19)
      The radiogenic isotope heterogeneity of oceanic basalts is often assessed using 2D isotope ratio diagrams. But because the underlying data are at least six dimensional (87Sr/86Sr, 143Nd/144Nd, 176Hf/177Hf, and 208,207,20 ...

    • Data reduction for X‐ray serial crystallography using machine learning 

      Rahmani, Vahid; Nawaz, Shah; Pennicard, David; Setty, Shabarish Pala Ramakantha; Graafsma, Heinz (Journal of Applied Crystallography, 2023-01-24)
      Serial crystallography experiments produce massive amounts of experimental data. Yet in spite of these large‐scale data sets, only a small percentage of the data are useful for downstream analysis. Thus, it is essential ...

    • Fast fitting of reflectivity data of growing thin films using neural networks 

      Greco, Alessandro; Starostin, Vladimir; Karapanagiotis, Christos; Hinderhofer, Alexander; Gerlach, Alexander; Pithan, Linus; Liehr, Sascha; Schreiber, Frank; Kowarik, Stefan (Journal of Applied Crystallography, 2019-11-08)
      X‐ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. This study shows how a simple artificial neural network model can be used to ...

    • Machine learning for scattering data: strategies, perspectives and applications to surface scattering 

      Hinderhofer, Alexander; Greco, Alessandro; Starostin, Vladimir; Munteanu, Valentin; Pithan, Linus; Gerlach, Alexander; Schreiber, Frank (Journal of Applied Crystallography, 2023-01-24)
      Machine learning (ML) has received enormous attention in science and beyond. Discussed here are the status, opportunities, challenges and limitations of ML as applied to X‐ray and neutron scattering techniques, with an ...

    • Modeling Viscosity of Volcanic Melts With Artificial Neural Networks 

      Langhammer, D.ORCIDiD; Di Genova, D.; Steinle‐Neumann, G.ORCIDiD (Geochemistry, Geophysics, Geosystems, 2022-12-01)
      Viscosity is of great importance in governing the dynamics of volcanoes, including their eruptive style. The viscosity of a volcanic melt is dominated by temperature and chemical composition, both oxides and water content. ...